U.S. patent application number 17/221489 was filed with the patent office on 2021-08-12 for systems and methods for reconstruction and rendering of viewpoint-adaptive three-dimensional (3d) personas.
This patent application is currently assigned to Verizon Patent and Licensing Inc.. The applicant listed for this patent is Verizon Patent and Licensing Inc.. Invention is credited to Cong Nguyen, Simion Venshtain, Yi Zhang.
Application Number | 20210248819 17/221489 |
Document ID | / |
Family ID | 1000005550331 |
Filed Date | 2021-08-12 |
United States Patent
Application |
20210248819 |
Kind Code |
A1 |
Venshtain; Simion ; et
al. |
August 12, 2021 |
SYSTEMS AND METHODS FOR RECONSTRUCTION AND RENDERING OF
VIEWPOINT-ADAPTIVE THREE-DIMENSIONAL (3D) PERSONAS
Abstract
An exemplary method includes maintaining a receiver-side
mesh-vertices list, receiving duplicative-vertex information from a
sender, and responsively reducing the receiver-side mesh-vertices
list in accordance with the received duplicative-vertex
information, and rendering, using the reduced receiver-side
mesh-vertices list, viewpoint-adaptive three-dimensional (3D)
personas of a subject at least in part by weighting video pixel
colors from different video-camera vantage points of video cameras
that capture video streams of the subject, the weighting being
performed according to a respective geometric relationship of each
video-camera vantage point to a user-selected viewpoint.
Inventors: |
Venshtain; Simion; (San
Mateo, CA) ; Zhang; Yi; (Chicago, IL) ;
Nguyen; Cong; (Chicago, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Verizon Patent and Licensing Inc. |
Basking Ridge |
NJ |
US |
|
|
Assignee: |
Verizon Patent and Licensing
Inc.
|
Family ID: |
1000005550331 |
Appl. No.: |
17/221489 |
Filed: |
April 2, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15865120 |
Jan 8, 2018 |
10997786 |
|
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17221489 |
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62542267 |
Aug 7, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2200/08 20130101;
H04N 7/157 20130101; H04L 29/06176 20130101; G06T 7/74 20170101;
G06K 9/6207 20130101; G06T 2207/30204 20130101; G06T 19/20
20130101; G06K 9/4642 20130101; G06T 19/003 20130101; H04N 13/271
20180501; G06T 2200/04 20130101; G06F 3/04815 20130101; G06T
2215/16 20130101; G06K 9/00228 20130101; G06T 2207/10028 20130101;
H04L 12/18 20130101; G06K 9/6289 20130101; G06K 9/00281 20130101;
H04N 7/147 20130101; G06T 2207/30201 20130101; G06T 2207/10024
20130101; G06T 15/20 20130101; H04L 65/604 20130101; G06T 7/75
20170101; G06K 9/00201 20130101; H04L 65/607 20130101; G06T 15/08
20130101; G06T 17/20 20130101; G06K 9/2018 20130101; H04N 7/142
20130101; H04N 13/254 20180501; H04N 13/214 20180501 |
International
Class: |
G06T 17/20 20060101
G06T017/20; G06K 9/00 20060101 G06K009/00; H04L 29/06 20060101
H04L029/06; G06K 9/46 20060101 G06K009/46; G06K 9/20 20060101
G06K009/20; G06K 9/62 20060101 G06K009/62; H04N 13/271 20180101
H04N013/271; H04N 13/254 20180101 H04N013/254; G06T 15/20 20110101
G06T015/20; G06T 19/20 20110101 G06T019/20; G06T 19/00 20110101
G06T019/00; H04N 7/14 20060101 H04N007/14; H04N 7/15 20060101
H04N007/15; G06T 7/73 20170101 G06T007/73; H04N 13/214 20180101
H04N013/214; G06F 3/0481 20130101 G06F003/0481 |
Claims
1. A method comprising: maintaining a receiver-side mesh-vertices
list; receiving duplicative-vertex information from a sender, and
responsively reducing the receiver-side mesh-vertices list in
accordance with the received duplicative-vertex information; and
rendering, using the reduced receiver-side mesh-vertices list,
viewpoint-adaptive three-dimensional (3D) personas of a subject at
least in part by weighting video pixel colors from different
video-camera vantage points of video cameras that capture video
streams of the subject, the weighting being performed according to
a respective geometric relationship of each video-camera vantage
point to a user-selected viewpoint.
2. The method of claim 1, wherein: each video stream in the video
streams includes video frames that are time-synchronized with video
frames of each of the other video streams in the video streams
according to a shared frame rate; each of the video cameras has a
known vantage point in a predetermined coordinate system; and the
method further comprises obtaining at least one 3D mesh of the
subject at the shared frame rate, the mesh including a plurality of
mesh vertices having respective known locations in the
predetermined coordinate system.
3. The method of claim 2, wherein obtaining the at least one 3D
mesh of the subject at the shared frame rate includes generating
the at least one 3D mesh of the subject at the shared frame
rate.
4. The method of claim 2, wherein obtaining the at least one 3D
mesh of the subject at the shared frame rate includes receiving the
at least one 3D mesh of the subject at the shared frame rate.
5. The method of claim 4, wherein receiving the at least one 3D
mesh of the subject at the shared frame rate includes receiving the
at least one 3D mesh of the subject at the shared frame rate in a
set of one or more geometric-data streams that is separate and
distinct from the video streams.
6. The method of claim 5, wherein serially rendering the generated
one or more 3D submeshes as overlays on one another includes
setting pixel color values for each rendered submesh so as to
cumulatively achieve a desired color weighting.
7. The method of claim 2, wherein rendering viewpoint-adaptive 3D
personas of the subject includes providing a graphics processing
unit (GPU) pipeline with data conveying, for respective mesh
vertices, location data in the predetermined coordinate system and
color-mapping data indexing into the video frames.
8. The method of claim 7, wherein the GPU pipeline includes a
LizardTech GPU or an OpenGL GPU pipeline.
9. The method of claim 1, wherein at least one of the video streams
is a raw video stream.
10. The method of claim 1, wherein at least one of the video
streams is an encoded video stream.
11. The method of claim 1, wherein rendering viewpoint-adaptive 3D
personas of the subject includes rendering viewpoint-adaptive 3D
personas of the subject as part of a virtual-reality (VR) or an
augmented-reality (AR) experience.
12. The method of claim 1, wherein rendering viewpoint-adaptive 3D
personas of the subject includes rendering viewpoint-adaptive 3D
personas of the subject as part of a 0.degree. to 360.degree.
viewer experience.
13. The method of claim 1, further comprising receiving
camera-extrinsic data from a data-capture location associated with
the subject, wherein rendering viewpoint-adaptive 3D personas of
the subject comprises rendering viewpoint-adaptive 3D personas of
the subject based at least in part on the received camera-extrinsic
data.
14. The method of claim 13, further comprising receiving
camera-intrinsic data from a data-capture location associated with
the subject, wherein rendering viewpoint-adaptive 3D personas of
the subject further includes rendering viewpoint-adaptive 3D
personas of the subject based at least in part on the received
camera-intrinsic data.
15. A system comprising: a processor; and data storage containing
instructions executable by the processor to: maintain a
receiver-side mesh-vertices list; receive duplicative-vertex
information from a sender, and responsively reducing the
receiver-side mesh-vertices list in accordance with the received
duplicative-vertex information; and render, using the reduced
receiver-side mesh-vertices list, viewpoint-adaptive
three-dimensional (3D) personas of a subject at least in part by
weighting video pixel colors from different video-camera vantage
points of video cameras that capture video streams of the subject,
the weighting being performed according to a respective geometric
relationship of each video-camera vantage point to a user-selected
viewpoint.
16. The system of claim 15, wherein: each video stream in the video
streams includes video frames that are time-synchronized with video
frames of each of the other video streams in the video streams
according to a shared frame rate; each of the video cameras has a
known vantage point in a predetermined coordinate system; and
instructions are further executable by the processor to obtain at
least one 3D mesh of the subject at the shared frame rate, the mesh
including a plurality of mesh vertices having respective known
locations in the predetermined coordinate system.
17. The system of claim 16, wherein the obtaining the at least one
3D mesh of the subject at the shared frame rate includes generating
the at least one 3D mesh of the subject at the shared frame
rate.
18. The system of claim 16, wherein the obtaining the at least one
3D mesh of the subject at the shared frame rate includes receiving
the at least one 3D mesh of the subject at the shared frame
rate.
19. The system of claim 18, wherein the receiving the at least one
3D mesh of the subject at the shared frame rate includes receiving
the at least one 3D mesh of the subject at the shared frame rate in
a set of one or more geometric-data streams that is separate and
distinct from the video streams.
20. The system of claim 16, wherein the rendering
viewpoint-adaptive 3D personas of the subject includes providing a
graphics processing unit (GPU) pipeline with data conveying, for
respective mesh vertices, location data in the predetermined
coordinate system and color-mapping data indexing into the video
frames.
Description
RELATED APPLICATIONS
[0001] This application is a continuation application of U.S.
patent application Ser. No. 15/865,120, filed Jan. 8, 2018, which
claims benefit of U.S. Provisional Patent Application No.
62/542,267, filed Aug. 7, 2017, each of which is herein
incorporated by reference in its entirety.
BACKGROUND INFORMATION
[0002] Interpersonal communication is a fundamental part of human
society. Historically significant developments in the area of
interpersonal communication include the invention of the telegraph,
the invention of the telephone, and the realization of
interpersonal communication over data connections, often via the
Internet. The continuing proliferation of personal communication
devices such as cellphones, smartphones, tablets, head-mounted
displays (HMDs), and the like has only furthered the ways in which
and the extent to which people communicate with one another, both
in one-to-one communication sessions and in one-to-many and
many-to-many conference communication sessions (e.g., sessions that
involve three or more endpoints).
[0003] Further developments have occurred in which both
visible-light-image (e.g., color-image) and depth-image data is
captured (perhaps as part of capturing sequences of video frames)
and combined in ways that allow extractions from two-dimensional
(2D) video of "personas" wherein the remainder of the visible
portion of video frames, such as the background outside of the
outline of the person has been removed. Persona extraction, or
"user extraction" is accordingly also known as "background removal"
and by other names. In some implementations, an extracted persona
is partially overlaid, typically on a pixel-wise basis, over a
different background, video stream, slide presentation, and/or the
like.
[0004] The following U.S. patents and U.S. Patent Application
Publications relate in various ways to persona extraction and
associated technologies. Each of them is hereby incorporated herein
by reference in its respective entirety. [0005] U.S. Pat. No.
9,628,722, issued Apr. 18, 2017 and entitled "Systems and Methods
for Embedding a Foreground Video into a Background Feed Based on a
Control Input;" [0006] U.S. Pat. No. 8,818,028, issued Aug. 26,
2014 and entitled "Systems and Methods for Accurate User Foreground
Video Extraction;" [0007] U.S. Pat. No. 9,053,573, issued Jun. 9,
2015 and entitled "Systems and Methods for Generating a Virtual
Camera Viewpoint for an Image;" [0008] U.S. Pat. No. 9,008,457,
issued Apr. 14, 2015 and entitled "Systems and Methods for
Illumination Correction of an Image;" [0009] U.S. Pat. No.
9,300,946, issued Mar. 29, 2016 and entitled "System and Method for
Generating a Depth Map and Fusing Images from a Camera Array;"
[0010] U.S. Pat. No. 9,055,186, issued Jun. 9, 2015 and entitled
"Systems and Methods for Integrating User Personas with Content
During Video Conferencing;" [0011] U.S. Patent Application
Publication No. 2015/0172069, published Jun. 18, 2015 and entitled
"Integrating User Personas with Chat Sessions;" [0012] U.S. Pat.
No. 9,386,303, issued Jul. 5, 2016 and entitled "Transmitting Video
and Sharing Content via a Network Using Multiple Encoding
Techniques;" [0013] U.S. Pat. No. 9,414,016, issued Aug. 9, 2016
and entitled "System and Methods for Persona Identification Using
Combined Probability Maps;" [0014] U.S. Pat. No. 9,485,433, issued
Nov. 1, 2016 and entitled "Systems and Methods for Iterative
Adjustment of Video-Capture Settings Based on Identified Persona;"
[0015] U.S. Patent Application Publication No. 2015/0188970,
published Jul. 2, 2015 and entitled "Methods and Systems for
Presenting Personas According to a Common Cross-Client
Configuration;" [0016] U.S. Pat. No. 8,649,592, issued Feb. 11,
2014 and entitled "System for Background Subtraction with 3D
Camera;" [0017] U.S. Pat. No. 8,643,701, issued Feb. 4, 2014 and
entitled "System for Executing 3D Propagation for Depth Image-Based
Rendering;" [0018] U.S. Pat. No. 9,671,931, issued Jun. 6, 2017 and
entitled "Methods and Systems for Visually Deemphasizing a
Displayed Persona;" [0019] U.S. Pat. No. 9,607,397, issued Mar. 28,
2017 and entitled "Methods and Systems for Generating a
User-Hair-Color Model;" [0020] U.S. Pat. No. 9,563,962, issued Feb.
7, 2017 and entitled "Methods and Systems for Assigning Pixels
Distance-Cost Values using a Flood Fill Technique;" [0021] U.S.
Patent Application Publication No. 2016/0343148, published Nov. 24,
2016 and entitled "Methods and Systems for Identifying Background
in Video Data Using Geometric Primitives;" [0022] U.S. Patent
Application Publication No. 2016/0353080, published Dec. 1, 2016
and entitled "Methods and Systems for Classifying Pixels as
Foreground Using Both Short-Range Depth Data and Long-Range Depth
Data;" [0023] U.S. Pat. No. 9,883,155 entitled "Methods and Systems
for Combining Foreground Video and Background Video Using Chromatic
Matching;" and [0024] U.S. Pat. No. 9,881,207 entitled "Methods and
Systems for Real-Time User Extraction Using Deep Learning
Networks."
SUMMARY
[0025] Presently disclosed are systems and methods for capturing,
transferring, and rendering viewpoint-adaptive 3D personas.
[0026] An embodiment includes a method that includes receiving one
or more video streams captured of a subject by one or more video
cameras, each video stream including video frames that are
time-synchronized with the video frames of each of the other video
streams according to a shared frame rate, each of the one or more
video cameras having a known vantage point in a predetermined
coordinate system; obtaining at least one three-dimensional (3D)
mesh of the subject at the shared frame rate, the mesh being
time-synchronized with the video frames of the video streams, the
mesh including a plurality of mesh vertices having respective known
locations in the predetermined coordinate system; identifying a
user-selected viewpoint, and responsively identifying a
viewpoint-specific subset of the mesh vertices visible from the
user-selected viewpoint, at the shared frame rate; generating one
or more 3D submeshes of the subject at the shared frame rate at
least in part by calculating one or more visible-vertices lists
from the vantage point of each video camera from which at least
part of the viewpoint-specific subset of mesh vertices is visible;
projecting one or more mesh vertices from the calculated
visible-vertices lists on to video pixels from the vantage points
of the one or more video cameras; and rendering viewpoint-adaptive
3D personas of the subject at the shared frame rate at least in
part by weighting video pixel colors from different video-camera
vantage points according to the respective geometric relationship
of each video-camera vantage point to the user-selected
viewpoint.
[0027] In one embodiment, the at least one of the received video
streams is a raw video stream.
[0028] In one embodiment, each of the received video streams is a
raw video stream.
[0029] In one embodiment, at least one of the received video
streams is an encoded video stream, and each of the received video
streams is an encoded video stream.
[0030] In one embodiment, obtaining the at least one 3D mesh of the
subject at the shared frame rate includes either generating the at
least one 3D mesh of the subject at the shared frame rate, or
receiving the at least one 3D mesh of the subject at the shared
frame rate; or receiving the at least one 3D mesh of the subject at
the shared frame rate in a set of one or more geometric-data
streams that is separate and distinct from the received video
streams.
[0031] In one embodiment, the rendering viewpoint-adaptive 3D
personas of the subject at the shared frame rate at least in part
by weighting video pixel colors from different video-camera vantage
points according to the respective geometric relationship of each
video-camera vantage point to the user-selected viewpoint includes
serially rendering the generated one or more 3D submeshes as
overlays on one another. The serially rendering the generated one
or more 3D submeshes as overlays on one another includes setting
pixel color values for each rendered submesh so as to cumulatively
achieve a desired color weighting.
[0032] In one embodiment, the rendering viewpoint-adaptive 3D
personas of the subject includes rendering viewpoint-adaptive 3D
personas of the subject as part of a virtual-reality (VR)
experience or an augmented-reality (AR) experience, or as part of a
360.degree. viewer experience, or as part of a
less-than-360.degree. experience.
[0033] In one embodiment, rendering viewpoint-adaptive 3D personas
of the subject includes providing a graphics processing unit (GPU)
pipeline with data conveying, for respective mesh vertices,
location data in the predetermined coordinate system and
color-mapping data indexing into the received video frames. For
example, the GPU pipeline can include a LizardTech GPU pipeline, an
OpenGL GPU pipeline.
[0034] In one embodiment, the method can include maintaining a
receiver-side mesh-vertices list, receiving duplicative-vertex
information from a sender, and responsively reducing the
receiver-side mesh-vertices list in accordance with the received
duplicative-vertex information, wherein rendering
viewpoint-adaptive 3D personas of the subject includes rendering
viewpoint-adaptive 3D personas of the subject using the reduced
receiver-side mesh-vertices list.
[0035] In one embodiment, the method includes receiving
camera-extrinsic data from a data-capture location associated with
the subject, wherein rendering viewpoint-adaptive 3D personas of
the subject includes rendering viewpoint-adaptive 3D personas of
the subject based at least in part on the received camera-extrinsic
data; and receiving camera-intrinsic data from a data-capture
location associated with the subject, wherein rendering
viewpoint-adaptive 3D personas of the subject further includes
rendering viewpoint-adaptive 3D personas of the subject based at
least in part on the received camera-intrinsic data.
[0036] In one embodiment, the method also includes receiving
camera-intrinsic data from a data-capture location associated with
the subject, wherein rendering viewpoint-adaptive 3D personas of
the subject comprises rendering viewpoint-adaptive 3D personas of
the subject based at least in part on the received camera-intrinsic
data.
[0037] Another embodiment relates to a receiving-and-rendering
system including a communication interface, a processor, and data
storage containing instructions executable by the processor for
causing the presenter server system to carry out a set of
functions, wherein the set of functions including receiving one or
more video streams respectively captured of a subject by one or
more video cameras, each video stream including video frames that
are time-synchronized with the video frames of each of the other
video streams according to a shared frame rate, each video camera
having a known vantage point in a predetermined coordinate system,
obtaining at least one three-dimensional (3D) mesh of the subject
at the shared frame rate, the at least one mesh being
time-synchronized with the video frames of the one or more video
streams, each mesh including a plurality of mesh vertices having
known locations in the predetermined coordinate system, identifying
a user-selected viewpoint, and responsively identifying a
viewpoint-specific subset of the mesh vertices visible from the
user-selected viewpoint, at the shared frame rate, generating one
or more 3D submeshes of the subject at the shared frame rate at
least in part by calculating visible-vertices lists from the
respective vantage point of each video camera from which at least
part of the viewpoint-specific subset of mesh vertices is visible,
projecting one or more mesh vertices from the calculated
visible-vertices lists on to video pixels from the vantage points
of the corresponding video cameras, and rendering
viewpoint-adaptive 3D personas of the subject at the shared frame
rate at least in part by weighting video pixel colors from
different video-camera vantage points according to the respective
geometric relationship of each video-camera vantage point to the
user-selected viewpoint.
[0038] Any of the variations and permutations described anywhere in
this disclosure can be implemented for any embodiments, including
for any method embodiments and for any system embodiments.
Furthermore, this flexibility and cross-applicability of
embodiments is present in spite of the use of slightly different
language (e.g., process, method, steps, functions, set of
functions, and/or the like) to describe and/or characterize such
embodiments.
[0039] In the present disclosure, one or more elements are referred
to as "modules" that carry out (e.g., perform, execute, and the
like) various functions that are described herein in connection
with the respective modules. As used herein, a module includes
hardware (e.g., one or more processors, one or more
microprocessors, one or more microcontrollers, one or more
microchips, one or more application-specific integrated circuits
(ASICs), one or more field programmable gate arrays (FPGAs), one or
more memory devices, and/or the like) deemed suitable by those of
skill in the relevant art for a given implementation. Each
described module also includes instructions executable by the
aforementioned hardware for carrying out the one or more functions
described herein as being carried out by the respective module.
Those instructions could take the form of or include hardware
(e.g., hardwired) instructions, firmware instructions, software
instructions, and/or the like, and may be stored in any suitable
non-transitory computer-readable medium or media, such as those
commonly referred to as random-access memory (RAM), read-only
memory (ROM), and/or the like.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] FIG. 1 is a schematic information-flow diagram depicting
data capture of an example presenter, and transmission of the
captured data, by a set of example video-and-depth cameras (VDCs),
as well as data receipt and presentation to a viewer of an example
viewpoint-adaptive 3D persona of the presenter by an example
head-mounted display (HMD), in accordance with at least one
embodiment.
[0041] FIG. 2 is a schematic information-flow diagram depicting an
example presenter server system (PSS) communicatively disposed
between the VDCs and the HMD of FIG. 1, in accordance with at least
one embodiment.
[0042] FIG. 3 is an input/output-(I/O)-characteristic block diagram
of PSS of FIG. 2, in accordance with at least one embodiment.
[0043] FIG. 4 is a first example functional-module-specific
I/O-characteristic block diagram of PSS of FIG. 2, in accordance
with at least one embodiment.
[0044] FIG. 5 is a functional-module-specific I/O-characteristic
block diagram of a second example PSS, in accordance with at least
one embodiment.
[0045] FIG. 6 is a hardware-architecture diagram of an example
computing-and-communication device (CCD), in accordance with at
least one embodiment.
[0046] FIG. 7 is a diagram of an example communication system, in
accordance with at least one embodiment.
[0047] FIG. 8 depicts an example HMD, in accordance with at least
one embodiment.
[0048] FIG. 9A is a first front view of an example camera-assembly
rig having mounted thereon four example camera assemblies, in
accordance with at least one embodiment.
[0049] FIG. 9B is a second front view of the camera-assembly rig
and camera assemblies of FIG. 9A, shown for an example reference
set of cartesian-coordinate axes, in accordance with at least one
embodiment.
[0050] FIG. 9C is a partial top view of the camera-assembly rig and
camera assemblies of FIG. 9A, shown with respect to the reference
set of cartesian-coordinate axes of FIG. 9B, in accordance with at
least one embodiment.
[0051] FIG. 9D is a partial front view of the camera-assembly rig
and camera assemblies of FIG. 9A, shown with respect to the
reference set of cartesian-coordinate axes of FIG. 9B, where each
such camera assembly is also shown with respect to its own example
camera-assembly-specific set of cartesian-coordinate axes, in
accordance with at least one embodiment.
[0052] FIG. 10A is a first front view of an example camera-assembly
rig having mounted thereon three example camera assemblies, in
accordance with at least one embodiment.
[0053] FIG. 10B is a second front view of the camera-assembly rig
and camera assemblies of FIG. 10A, shown with respect to an example
reference set of cartesian-coordinate axes, in accordance with at
least one embodiment.
[0054] FIG. 10C is a partial top view of the camera-assembly rig
and camera assemblies of FIG. 10A, shown with respect to the
reference set of cartesian-coordinate axes of FIG. 10B, in
accordance with at least one embodiment.
[0055] FIG. 10D is a partial front view of the camera-assembly rig
and camera assemblies of FIG. 10A, shown with respect to the
reference set of cartesian-coordinate axes of FIG. 10B, where each
such camera assembly is also shown with respect to its own example
camera-assembly-specific set of cartesian-coordinate axes, in
accordance with at least one embodiment.
[0056] FIG. 11A is a first front view of an example one of the
camera assemblies of FIG. 10A, in accordance with at least one
embodiment.
[0057] FIG. 11B is a second front view of the camera assembly of
FIG. 11A, shown with respect to an example portion of the reference
set of cartesian-coordinate axes of FIG. 10B, in accordance with at
least one embodiment.
[0058] FIG. 11C is a modified virtual front view of the camera
assembly of FIG. 11A, also shown with respect to the portion from
FIG. 11B of the reference set of cartesian-coordinate axes of FIG.
10B, in accordance with at least one embodiment.
[0059] FIG. 12 is a diagram of a first example presenter scenario
in which the presenter of FIG. 1 is positioned in an example room
in front of the camera-assembly rig and camera assemblies of FIG.
10A, in accordance with at least one embodiment.
[0060] FIG. 13 is a diagram of a second example presenter scenario
in which the presenter of FIG. 1 is positioned on an example stage
in front of the camera-assembly rig and camera assemblies of FIG.
10A, in accordance with at least one embodiment.
[0061] FIG. 14 is a diagram of a first example viewer scenario
according to which a viewer is using HMD of FIG. 1 to view the 3D
persona of FIG. 1 of the presenter of FIG. 1 as part of an example
virtual-reality (VR) experience, in accordance with at least one
embodiment.
[0062] FIG. 15 is a diagram of a second example viewer scenario
according to which a viewer is using HMD of FIG. 1 to view the 3D
persona of FIG. 1 of the presenter of FIG. 1 as part of an example
augmented-reality (AR) experience, in accordance with at least one
embodiment.
[0063] FIG. 16A is a flowchart of a first example method, in
accordance with at least one embodiment.
[0064] FIG. 16B is a flowchart of a second example method, in
accordance with at least one embodiment.
[0065] FIG. 16C is a second example functional-module-specific
I/O-characteristic block diagram of PSS of FIG. 2, in accordance
with at least one embodiment.
[0066] FIG. 16D is the hardware-architecture diagram of FIG. 6
further including a facial-mesh model storage, in accordance with
at least one embodiment.
[0067] FIG. 17 is a perspective diagram depicting a view of a first
example projection from a focal point of an example one of the
camera assemblies of FIG. 10A through the four corners of a
two-dimensional (2D) pixel array of the example camera assembly on
to the reference set of cartesian-coordinate axes of FIG. 10B, in
accordance with at least one embodiment.
[0068] FIG. 18 is a perspective diagram depicting a view of a
second example projection from the focal point of FIG. 17 through
the centroid of the 2D pixel array of FIG. 17 on to the reference
set of cartesian-coordinate axes of FIG. 10B, in accordance with at
least one embodiment.
[0069] FIG. 19 is a perspective diagram depicting a view of a third
example projection from the focal point of FIG. 17 through an
example pixel in the 2D pixel array of FIG. 17 on to the reference
set of cartesian-coordinate axes of FIG. 10B, in accordance with at
least one embodiment.
[0070] FIG. 20 is a flowchart of a third example method, in
accordance with at least one embodiment.
[0071] FIG. 21 is a first view of an example submesh of a subject,
shown with respect to the reference set of cartesian-coordinate
axes of FIG. 10B, in accordance with at least one embodiment.
[0072] FIG. 22 is a second view of the submesh of FIG. 21, as well
as a magnified portion thereof, in accordance with at least one
embodiment.
[0073] FIG. 23 is a flowchart of a fourth example method, in
accordance with at least one embodiment.
[0074] FIG. 24 is a view of an example viewer-side arrangement
including three example submesh virtual-projection viewpoints that
correspond respectively with the three camera assemblies of FIG.
10A, in accordance with at least one embodiment.
[0075] FIG. 25 is a view of the viewer-side arrangement of FIG. 24
in which a viewer has selected a center viewpoint, in accordance
with at least one embodiment.
[0076] FIG. 26 is a view of the viewer-side arrangement of FIG. 24
in which a viewer has selected a rightmost viewpoint, in accordance
with at least one embodiment.
[0077] FIG. 27 is a view of the viewer-side arrangement of FIG. 24
in which a viewer has selected a leftmost viewpoint, in accordance
with at least one embodiment.
[0078] FIG. 28 is a view of the viewer-side arrangement of FIG. 24
in which a viewer has selected an example intermediate viewpoint
between the center viewpoint of FIG. 25 and the leftmost viewpoint
of FIG. 27, in accordance with at least one embodiment.
[0079] FIG. 29 is a view of the viewer-side arrangement of FIG. 24
in which a viewer has selected an example intermediate viewpoint
between the center viewpoint of FIG. 25 and the rightmost viewpoint
of FIG. 26, in accordance with at least one embodiment.
[0080] The entities, connections, arrangements, and the like that
are depicted in and described in connection with the various
figures are presented by way of example and not limitation. As
such, any and all statements or other indications as to what a
particular figure "depicts," what a particular element or entity in
a particular figure "is" or "has," and any and all similar
statements--that may in isolation and out of context be read as
absolute and therefore limiting--can only properly be read as being
constructively preceded by a clause such as "In at least one
embodiment . . . ." And it is for reasons akin to brevity and
clarity of presentation that this implied leading clause is not
repeated in the below detailed description of the drawings.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
I. Introduction
[0081] In addition to persona extraction from a 2D combination of
visible-light-image and depth-image data, it is also possible to
use multiple visible-light cameras and multiple depth cameras that
can be combined in sets that can include at least one of each, for
example, in "camera assemblies," a term that is further defined
below--positioned at multiple viewpoints around a subject (e.g., a
person) to capture enough visible-light data and depth data to
render a 3D representation of the subject. That 3D representation,
referred to herein as a 3D persona, could be rendered to a viewer
at a remote location (e.g., at a location that is remote with
respect to the location of the subject). As used herein, the
subject thus "teleports" to the remote location, virtually, not
corporeally.
[0082] With virtual teleportation, there are tradeoffs such as
resolution vs. effective data-transfer rate (the transfer on
average of a given quantum of data per a given unit of time, a
ratio that depends on factors such as available bandwidth and
efficiency of use). Higher resolution produces more visually
impressive results but typically requires a higher effective
data-transfer rate, lower resolution requires a lower effective
data-transfer rate and decreases the end-user experience.
[0083] According to a first scenario, two people at two different
locations are communicating. For simplicity of explanation and not
by way of limitation, this first example scenario involves
substantially one-way data communication from a first person
(referred to in connection with this example as "the presenter") to
a second person (referred to in connection with this example as
"the viewer").
[0084] In this example, the presenter is giving an astronomy
lecture from the first location (e.g., a lecture hall), at which
suitable data-capture equipment (perhaps a camera-assembly rig
having multiple camera assemblies mounted thereon, examples of both
of which are described herein) has been installed or otherwise set
up, while the viewer is viewing this lecture in realtime, or
substantially live, from the second location (e.g., their home)
using an HMD. It is not necessary that the viewer be using an HMD,
nor is it necessary that the viewer be viewing the lecture in
realtime, as these are examples. The viewer could be viewing the
lecture via one or more screens of any type and/or any other
display technology deemed suitable by those of skill in the art for
a given context or in a given implementation. The viewer could be
viewing the lecture any amount of time after it actually
happened--e.g., the viewer could be streaming the recorded lecture
from a suitable server. And numerous other arrangements are
possible as well.
[0085] As explained herein, the viewer can change their viewing
angle (e.g., by walking around, turning their head, changing the
direction of their gaze, operating a joystick, operating a control
cross, operating a keyboard, operating a mouse, and/or the like)
and be presented with color-accurate and depth-accurate renderings
of a 3D persona of the presenter ("a 3D presenter persona") from
the viewer's selected viewing angle ("a viewpoint-adaptive 3D
persona, or, "a viewpoint-adaptive 3D presenter persona"). Herein,
the adjective "viewpoint-adaptive" is not used to qualify every
occurrence of "3D persona," "3D presenter persona," and the like;
but to enhance readability.
[0086] As examples, the 3D presenter persona is shown to appear to
the viewer to be superimposed on a background (e.g., the lunar
surface) as part of a virtual-reality (VR) experience, or
superimposed at the viewer's location as part of an
augmented-reality (AR) experience. If the data-capture equipment at
the first location is sufficiently comprehensive, the viewer may be
able to virtually "walk" all around the 3D presenter persona--the
viewer may be provided with a 360.degree. 3D virtual
experience.
[0087] Other data-capture-equipment arrangements are contemplated,
including three, four or multi-camera assemblies--including both
visible-light-camera equipment and depth-camera equipment arranged
on a rigid physical structure referred to herein as a
camera-assembly rig positioned in front of the presenter able to
capture the presenter from each of a set of vantage points such as
left, right, and center. Top-center and bottom-center can be
included in a four-camera-assembly rig. Other rigs are also
possible, including six or more cameras located at vantage points
as needed in a given location. For example, in some embodiments,
45.degree. angles could be desirable and the number of cameras
could therefore multiply as needed. Furthermore, cameras focusing
on a feature of a presenter could be added to a rig and the
geometry for such cameras can be calculated to provide necessary
integration with the other cameras in the rig.
[0088] In some embodiments, such as those in which the
camera-assembly equipment is mounted on a camera-assembly rig
(e.g., embodiments in which no visible-light-camera equipment or
depth-camera equipment other than that which is mounted on the
camera-assembly rig at the data-capture location), 3D presenter
persona can be presented to the viewer in a less-than-360.degree.
3D virtual experience.
[0089] Two-way (and more than two-way) virtual-teleportation
sessions are contemplated, though one-way virtual-teleportation
sessions are also described herein, to simplify the explanation of
the present systems and methods.
[0090] Returning now to the first-described example scenario,
reference is made to FIG. 1, which is a schematic information-flow
diagram depicting data capture of an example presenter 102, and
transmission of the captured data, by a set of example VDCs 106A
("Alpha"), 106B ("Beta"), and 106.sub..left brkt-top. ("Gamma"), as
well as data receipt and presentation to a viewer (not depicted) of
an example viewpoint-adaptable 3D persona 116 of the presenter 102
by an example HMD 112, in accordance with at least one embodiment.
The set of VDCs 106A, 106B, and 106.sub..GAMMA. are referred to
herein using an abbreviation such as "the VDCs 106AB.sub..GAMMA.,"
"the VDCs 106A-.sub..GAMMA.," "the VDCs 106," and/or the like. One
of the VDCs 106 may be referred to specifically by its particular
reference numeral. The Greek letters Alpha ("A"), Beta ("B"), and
Gamma (".sub..GAMMA.") refer to various elements in FIG. 1 to
convey that these could be any three arbitrary vantage points of
the presenter 102, and are not meant to bear any relation to
concepts such as left, center, right, and/or the like.
[0091] As can be seen in FIG. 1, the presenter 102 is located in a
presenter location 104 (e.g., the above-mentioned lecture hall). At
the presenter location 104, the respective VDCs 106 are capturing
both video and depth data of the presenter 102, as represented by
the dotted arrows 107A, 107B, and 107.sub..GAMMA.. Each of the
arrows 107 is depicted as double-ended to indicate a two-way flow
of information. As described more fully below, each of the VDCs 106
may include an illuminator that project a pattern of infrared light
in the direction of the presenter 102 and then gather the
reflection of that pattern using multiple depth cameras and
stereoscopically analyze the collected data as part of a
depth-camera system of a given VDC 106. And each VDC 106 is using
its respective video-camera capability to capture visible-light
video of the presenter 102.
[0092] Each of the VDCs 106 is capturing such video and depth data
of the presenter 102 from their own respective vantage point at the
presenter location 104. The VDCs 106 transmit encoded video streams
108A, 108B, and 108.sub..GAMMA. to HMD 112, located at a viewer
location 113 (e.g., the above-mentioned home of the viewer). As
also shown in FIG. 1, the VDCs 106 are transmitting depth-data
streams 110A, 110B, and 110.sub..GAMMA. to HMD 112. At the viewer
location 113, HMD 112 uses the video streams 108AB.sub..GAMMA. and
the depth-data streams 110AB.sub..GAMMA. to render the
viewpoint-adaptive 3D persona 116 of the presenter 102 on a display
114 of HMD 112. As to the depiction of the display 114, the
reference letters W, X, Y, and Z are shown in FIG. 1 to convey that
the view of the display 114 shown in FIG. 1 is depicted as the
viewer would see it while wearing HMD 112.
[0093] FIG. 1 displays a high-level conceptual view 100 of an
embodiment in which both video and depth data of the presenter 102
is captured by each of multiple VDCs 106. This video and depth data
is transmitted using multiple distinct data streams from the
respective VDCs 106 to HMD 112, and the video and depth data is
combined by HMD 112 in rendering the viewpoint-adaptive 3D persona
116 of the presenter 102 on the display 114. 3D persona 116 is
shown standing on a lunar surface with a backdrop of stars in a
simplified depiction of a VR experience.
[0094] Data capture, transmission, and rendering functions can be
distributed in various ways as suitable by those of skill in the
art along the communication path between and including the
data-capture equipment (e.g., the VDCs 106) and the
persona-rendering equipment (e.g., HMD 112). In different
embodiments, one or more servers (and/or other suitable processing
devices, systems, and/or the like) are located at the data-capture
location, the data-rendering location, and/or in between, and the
herein-described functions can be distributed in various ways among
those servers, the data-capture equipment, the data-rendering
equipment, and/or other equipment.
II. Example Architecture
[0095] A. Example Presenter Server System (PSS)
[0096] An example of a server being communicatively disposed on the
communication path between the data-capture equipment and the
data-rendering equipment is depicted in FIG. 2, which is a
schematic information-flow diagram depicting a view 200 of an
embodiment in which an example presenter server system (PSS) 202 is
communicatively disposed between a set of VDCs 206 and HMD 112.
Many of the elements depicted in FIG. 2 are also depicted in FIG.
1.
[0097] One difference from FIG. 1 to FIG. 2 is that the VDCs 106
are replaced by VDCs 206. Because the information flow in this
embodiment differs from the information flow depicted and described
in connection with FIG. 1, different reference numerals identify
devices carrying out different sets of functions. Unlike the
AB.sub..GAMMA. notation used for the VDCs 106 of FIG. 1, the VDCs
206 of FIG. 2 use an LCR notation to specifically denote "left,"
"center," and right," though there is no serious attempt (other
than sequential arrangement) in FIG. 2 to depict the VDCs 206L
("left"), 206C ("center"), and 206R ("right") capturing a left-side
view, a centered view, and a right-side view, respectively, of the
presenter 102. Aside from the AB.sub..GAMMA. notation and the LCR
notation, the data-capture function is still carried out in
substantially the same way in the embodiment of FIG. 2 as it is in
the embodiment of FIG. 1. Also common to FIG. 1 and FIG. 2 are the
presenter 102, the presenter location 104, HMD 112, the viewer
location 113, the display 114, and the 3D presenter persona
116.
[0098] One difference between FIG. 1 and FIG. 2 is the presence in
FIG. 2 of PSS 202. In various embodiments, PSS 202 could reside at
the presenter location 104, the viewer location 113, or anywhere in
between. Regarding FIG. 2, an embodiment is described in which PSS
202 resides at the presenter location 104. Accordingly, each of the
video streams 208L, 208C, and 208R can include a "raw" video
stream, in that it is not compressed or truncated; in other words,
the video streams 208LCR can include full, standalone color frames
(images) (encoded in a well-known color space such as RGB, RGB-A,
or the like), in which none of the frames reference any one of the
other frames.
[0099] In some embodiments, each of the VDCs 206 transmits a
respective depth-data stream 210 to PSS 202. In embodiments in
which this depth data is gathered stereoscopically by each VDC 206
using multiple infrared (IR) cameras to gather reflection of a
single projected IR pattern, the VDCs 206 themselves could resolve
these stereoscopic differences in hardware and transmit depth-pixel
images to PSS 202 in the respective depth-data streams 210; it
could instead be the case that the VDCs 206 transmit raw IR images
to PSS 202, which then stereoscopically resolves pairs of IR images
to arrive at depth-pixel images that correspond with the
visible-light video images. Other example implementations are
possible.
[0100] In various embodiments, the capture and processing of video
and depth data are time-synchronized according to a shared frame
rate across the various data-capture equipment (e.g., the VDCs 106,
the VDCs 206, the hereinafter-described camera assemblies, and/or
the like), data-processing equipment (e.g., PSS 202), and
data-rendering equipment (e.g., HMD 112).
[0101] Data transfer between various entities or any
data-processing steps is not necessarily carried out by the
entities instantaneously. In some embodiments, there is
time-synchronized coordination whereby, for example, each instance
of data-capture equipment captures one frame (e.g., one video image
and a contemporaneous depth image) of the presenter 102 every fixed
amount of time, which is referred to herein as "the
shared-frame-rate period" (or perhaps just "the period"), and it is
the inverse of the shared frame rate, as is known in the art. In
one embodiment, 3D-mesh generation, data transmission, and
rendering functions also step along according to this shared frame
rate.
[0102] Depending on factors such as the length of the
shared-frame-rate period, the available computing speed and power,
and/or the time needed to carry out various functions, capture,
processing, and transmission (e.g., at least the sending) for a
given frame x could all occur within a single period. In other
embodiments, more of an assembly-line approach is used, whereby one
entity (e.g., PSS 202) may be processing a given frame x during the
same period that the data-capture equipment (e.g., the collective
VDCs 206) is capturing the next frame x+1. And certainly numerous
other timing examples could be given.
[0103] In the embodiment that is described herein in connection
with FIG. 2, PSS 202 transmits an encoded video stream 218
corresponding to each raw video stream 208 that PSS 202 receives
from a respective VDC 206. As described herein, PSS 202 may encode
a given raw video stream 208 as a corresponding encoded video
stream 218 in a number of different ways. Some known video-encoding
algorithms (a.k.a. "codecs" or "video codecs") include (i) those
developed by the "Moving Picture Experts Group" (MPEG), which
operates under the mutual coordination of the International
Standards Organization (ISO) and the International
Electro-Technical Commission (IEC), (ii) H.261 (a.k.a. Px64) as
specified by the International Telecommunication Union (ITU), and
(iii) H.263 as also specified by the ITU, though certainly others
could be used as well.
[0104] In some embodiments, each video camera (or video-camera
function of each VDC, camera assembly, or the like) captures its
own video stream, and each of those video streams is encoded
according to a (known or hereinafter-developed) standard video
codec for transmission in a corresponding distinct encoded video
stream for delivery to the rendering device. The video-capture and
video-encoding modules and/or equipment of various embodiments of
the present methods and systems need know nothing of one another,
including shared geometry, 3D-mesh generation, viewpoint-adaptive
rendering, and so on; they capture, encode (e.g., compress), and
transmit video.
[0105] Each respective depth-data stream 210 could include two
streams of raw IR images captured by two different IR cameras in
each VDC 206, for stereoscopic resolution thereof by PSS 202
include depth images of depth pixels that are generated at each VDC
206 using, e.g., VDC-hardware processing to create stereoscopic
resolution of pairs of time-synchronized IR images. In one
embodiment shown in FIG. 2, VDC-hardware-based stereoscopic
resolution of pairs of IR images into depth-pixel images are then
transmitted to PSS 202 for further processing. In some embodiments,
the VDCs capture RGB images in time-synchrony with the two IR
images and create a depth-pixel image.
[0106] Depth images could be captured by two IR cameras in a VDC.
In other embodiments, depth images can be created by using a single
IR camera. For example, a single IR camera transfers IR images to
create depth images after combination with other IR images captured
by different VDCs. Thus, multiple IR data streams can combine to
create a depth stream outside of the VDCs or DCS, for example, if
only one IR camera is present in each VDC. Thus, inexpensive VDCs
can be utilized to create stereoscopic 3D video without requiring a
two IR camera VDC.
[0107] Along with the encoded video streams 218, PSS 202 is
depicted in FIG. 2 as transmitting one or more geometric-data
streams 220LCR to HMD 112. There could be three separate streams
220L, 220C, and 220R, or it could instead be a single data stream
220LCR; and certainly other combinations could be implemented and
listed here as well. Regardless of stream count and arrangement,
this set of one or more geometric-data streams is referred to
herein as "the geometric-data stream 220LCR." Matters that are
addressed in the description of ensuing figures include (i) example
ways in which PSS 202 could generate the geometric-data stream
220LCR from the depth-data streams 210 and (ii) example ways in
which HMD 112 could use the geometric-data stream 220LCR in
rendering the viewpoint-adaptive 3D presenter persona 116.
[0108] A more scale-independent and explicitly mathematically
expressed version of the I/O characteristics of PSS 202 is shown in
FIG. 3, which is an input/output-(I/O)-characteristic block diagram
of PSS 202 of FIG. 2, in accordance with at least one embodiment.
From FIG. 2 to FIG. 3 PSS 202 is shown, note that other elements
that are depicted in FIG. 3 are numbered in the 300 series to
correspond with the numbering in the 200 series elements in FIG.
2.
[0109] The VDCs 206 of FIG. 2 are replaced in FIG. 3 by the
separating the video components from the depth components. FIG. 2
shows a set of M video cameras (VCs) 306V and a set of N
depth-capture cameras (DCs) 306D. The raw video streams 208L, 208C
and 208R of FIG. 2 are shown in FIG. 3 by M raw video streams 308,
each of which is expressed in FIG. 3 using the notation VSM(fx),
where VS stands for "video stream," M identifies the video camera
associated with the corresponding video stream 308, and fx notation
"frame x" indicates that the video streams 308 are
time-synchronized according to a shared frame rate. (each video
camera 306V captures the same numbered frame at the same time). The
depth capture streams 210L, 210C and 21OR of FIG. 2 are shown in
FIG. 3 by N depth-data streams 310, each of which is expressed in
FIG. 3 using the notation DDSN(fx), where DDS stands for
"depth-data stream," N identifies the depth-data camera associated
with the corresponding depth-data stream 310, and fx notation
"frame x" indicates that the depth-data streams 310 are
time-synchronized according to a shared frame rate.
[0110] The encoded video streams 218 of FIG. 2 are replaced in FIG.
3 by the M encoded video stream(s) 318, each of which is expressed
in FIG. 3 using the notation EVSM(fx), where (i) EVS stands for
"encoded video stream," (ii) M identifies the video camera, and
(iii) fy indicates "frame y," that the raw video streams 308 are
time-synchronized according to the shared frame rate. Per the above
timing discussion, y is equal to x-a, where a is an integer greater
than or equal to zero; in other words, "frame y" and "frame x"
could be the same frame, or "frame y" could be the frame captured
one or more frames prior to "frame x."
[0111] As depicted in FIG. 3, the DCs 306D transmit one or more
depth-data streams (DDS(s)) 310 to PSS 202. The one or more DDS(s)
310 (hereinafter "DDS 310") in FIG. 3 replace the depth-data
streams 210 of FIG. 2. In one embodiment, DDS 310 is in frame
synchrony--time synchrony according to a shared frame rate--with
one or more of the raw video streams 308, and is expressed in FIG.
3 using the notation DDS(fx). The geometric-data stream 220LCR of
FIG. 2 is replaced in FIG. 3 by the (similarly one or more)
geometric-data stream(s) 320 (referred to hereinafter as "the
geometric-data stream 320" whether it includes one stream of
geometric data or more than one stream of geometric data). The
geometric-data stream 320 is expressed in FIG. 3 as GEO(fy) to
indicate frame synchrony with each of the encoded video streams
318.
[0112] The data-capture equipment in FIG. 1 and FIG. 2 take the
form of multiple VDCs 106 and multiple VDCs 206, respectively. The
terms "VDC" and "camera assembly" are used interchangeably in this
description to refer to instantiations of hardware that each
include at least a visible-light (e.g., RGB) video camera and a
depth-camera system (e.g., an IR illuminator and one or two IR
cameras, the IR images from which are stereoscopically resolved to
produce depth images/depth-pixel images/arrays of depth pixels.
Likewise there are multiple depth-capture equipment options. FIG. 3
illustrates a separation of video-capture equipment (VCs 306V) and
depth-capture equipment (DCs 306D), however, depth-capture
equipment DCs 306D can be located near each VC 306V or apart from a
VC 306V.
[0113] These multiple different depicted data-capture-equipment
arrangements convey at least the point that combined
video-and-depth-capture equipment assemblies (e.g., VDCs, camera
assemblies, and the like) are an option but not the only option.
Video could be captured from some number of separate
video-data-capture vantage points and depth information could be
captured from some (perhaps different) number of (perhaps
different) depth-data-capture vantage points. There could be one or
more combined video-and-depth data-capture vantage points, one or
more video-data-capture-only vantage points, and/or one or more
depth-data-capture-only vantage points.
[0114] Thus, the DCs 306D could take forms such as a depth camera
substantially co-located with every respective video camera 306V, a
set of depth cameras, each of which may or may not be co-located
with a respective video camera 306V, and/or any other arrangement
of depth-data-capture equipment deemed suitable by those of skill
in the art for a given implementation. Moreover, stereoscopic
resolution is but one of a number of different depth-determination
technologies that could be used in combination, as known to those
of skill in the art.
[0115] The DDS 310 could take forms such as (i) a stream--that is
frame-synchronized (in frame synchrony) with each raw video stream
308--from each of multiple depth-camera systems (or camera
assemblies) of respective pairs of raw, time-synchronized IR images
in need of stereoscopic resolution, (ii) a stream--that is
frame-synchronized with each raw video stream 308--from each of
multiple depth-camera systems (or camera assemblies) of depth-pixel
images (that may be the result of stereoscopic resolution of
corresponding pairs of IR images), (iii) a stream--that is
frame-synchronized with each raw video stream 308--of 3D meshes of
the subject (such as presenter 102) in embodiments in which the DCS
306D includes both depth-data-capture equipment and generates 3D
meshes of a subject from depth data gathered from multiple vantage
points of the subject. In various different embodiments, PSS 202
obtains frame-synchronized 3D meshes of the subject by receiving
such 3D meshes from another entity such as the DCs 306D or by
generating such 3D meshes from raw or processed depth data captured
of the subject from multiple different vantage points. And other
approaches could be used as well.
[0116] In one embodiment, frame (fx) from one or more VCs combine
to create a "super frame" 308 that is a combination of video. Thus,
according to one embodiment, a super frame represents a video
sequence that only has to be encoded in PSS 202 one time. Likewise,
output streams from PSS 202 can be combined in a single stream
318.
[0117] PSS 202 may be architected in terms of different functional
modules, one example of which is depicted in FIG. 4, which is a
functional-module-specific I/O-characteristic block diagram of PSS
202, in accordance with at least one embodiment. Many aspects of
FIG. 4 are also depicted in FIG. 3. What is different in FIG. 4 is
that PSS 202 is specifically shown as including a
geometric-calculation module 402 and a video-encoding module
404.
[0118] In various different embodiments, the geometric-calculation
module 402 receives the DDS 310 from the DCs 306D, obtains (or
generates) 3D meshes of presenter 102 from received DDS 310,
generates geometric-data stream 320, and transmits one or more
geometric-data streams from PSS 202 to HMD 112. Depending on the
distribution of functionality, geometric-calculation module 402 may
stereoscopically resolve associated pairs of IR images to generate
depth frames.
[0119] In various different embodiments, the video-encoding module
404 carries out functions such as receiving the raw video streams
308 from video cameras 306V, encoding each of those raw video
streams 308 into an encoded video stream EVS using a suitable video
codec, and transmitting the generated encoded video streams EVS
from PSS 202 to HMD 112 separately or in a single stream 318.
[0120] Another possible functional-module architecture of a PSS is
shown in FIG. 5, which is a functional-module-specific
I/O-characteristic block diagram of a second example PSS 502, in
accordance with at least one embodiment. FIG. 5 is similar in many
ways to FIG. 4, other than that PSS 502 is shown as including not
only the geometric-calculation module 402 and the video-encoding
module 404, but also a data-capture module 502 that includes the M
video cameras 306V and N depth cameras DCs 306D. Thus, a PSS
according to the present disclosure could include the
video-data-capture equipment, and could include the depth
data-capture equipment.
[0121] B. Example Computing-and-Communication Device (CCD)
[0122] FIG. 4 and FIG. 5 depict a functional-module architecture of
PSS 202 and a possible functional-module architecture of PSS 502.
FIG. 6 illustrates a hardware-architecture diagram of an example
CCD 600, in accordance with an embodiment. A number of the devices
described herein are CCDs (computing-and-communication devices).
CCDs encompass mobile devices such as smartphones and tablets,
personal computers (PCs) such as laptops and desktops, networked
servers, devices designed for more specific purposes such as
visible-light (e.g., RGB) cameras and depth cameras, devices such
as HMDs usable in VR and AR contexts, and/or any other CCD(s)
deemed suitable by those of skill in the art.
[0123] CCDs herein include but are not limited to the following:
any or all of the VDCs 106, HMD 112, PSS 202, any or all of the
VDCs 206, the DCS 306D, any or all of the video cameras 306V, any
or all of the CCDs 704-710, any or all of the camera assemblies
924, any or all of the camera assemblies 1024, and any or all of
the projection elements 2404.
[0124] CCD 600 includes a communication interface 602, a processor
604, a data storage 606 containing program instructions 608 and
operational data 610, a user interface 612, a peripherals interface
614, and peripheral devices 616. Communication interface 602 may be
operable for communication according to one or more
wireless-communication protocols, some examples of which include
Long-Term Evolution (LTE), IEEE 802.11 (Wi-Fi), Bluetooth, and the
like. Communication interface 602 may also or instead be operable
for communication according to one or more wired-communication
protocols, some examples of which include Ethernet and USB.
Communication interface 602 may include any necessary hardware
(e.g., chipsets, antennas, Ethernet interfaces, etc.), any
necessary firmware, and any necessary software for conducting one
or more forms of communication with one or more other entities as
described herein.
[0125] Processor 604 may include one or more processors of any type
deemed suitable by those of skill in the relevant art, some
examples including a general-purpose microprocessor and a dedicated
digital signal processor (DSP).
[0126] The data storage 606 may take the form of any non-transitory
computer-readable medium or combination of such media, some
examples including flash memory, RAM, and ROM to name but a few, as
any one or more types of non-transitory data-storage technology
deemed suitable by those of skill in the relevant art could be
used. As depicted in FIG. 6, the data storage 606 contains program
instructions 608 executable by the processor 604 for carrying out
various functions described herein, and further is depicted as
containing operational data 610, which may include any one or more
data values stored by and/or accessed by the CCD 600 in carrying
out one or more of the functions described herein.
[0127] The user interface 612 may include one or more input devices
and/or one or more output devices. User interface 612 may include
one or more touchscreens, buttons, switches, microphones,
keyboards, mice, touchpads, and/or the like. For output devices,
the user interface 612 may include one or more displays, speakers,
light emitting diodes (LEDs), speakers, and/or the like. One or
more components of the user interface 612 could provide both
user-input and user-output functionality, a touchscreen being one
example.
[0128] Peripherals interface 614 could include any wired and/or any
wireless interface for communicating with one or more peripheral
devices such as input devices, output devices, I/O devices, storage
devices, still-image cameras, video cameras, webcams, speakers,
depth cameras, IR illuminator, HMDs, and/or any other type of
peripheral device deemed suitable by those of skill in the art for
a given implementation. Some example peripheral interfaces include
USB, FireWire, Bluetooth, HDMI, DisplayPort, mini DisplayPort, and
the like. Other example peripheral devices and peripheral
interfaces could be listed.
[0129] Peripherals interface 614 of CCD 600 could have one or more
peripheral devices 616 permanently or at least semi-permanently
installed as part of the hardware architecture of the CCD 600. The
peripheral devices 616 could include peripheral devices mentioned
in the preceding paragraph and/or any type deemed suitable by those
of skill in the art.
[0130] C. Example Communication System
[0131] FIG. 7 depicts an example communication system 700. In FIG.
7, four CCDs 704, 706, 708, and 710 are communicatively
interconnected with one another via network 702. The CCD 704 is
connected to network 702 via a communication link 714, CCD 706 via
a communication link 716, CCD 708 via a communication link 718, and
CCD 720 via a communication link 720. Any one or more of the
communication links 714-720 could include one or more
wired-communication links, one or more wireless-communication
links, one or more switches, routers, bridges, other CCDs, and/or
the like.
[0132] D. Example Head-Mounted Display (HMD)
[0133] FIG. 8 depicts HMD 112 in accordance with at least one
embodiment. HMD 112 includes a strap 802, an overhead piece 804, a
face-mounting mask 806, and the aforementioned display 114. Other
HMDs could include different components, as HMD 112 in FIG. 8 is
provided by way of example and not limitation. As a general matter,
the strap 802 and the overhead piece 804 cooperate with the
face-mounting mask 806 to secure HMD 112 to the viewer's head such
that the viewer can readily observe the display 114. Some examples
of commercially available HMDs that could be used as HMD 112 in
connection with embodiments of the present systems and methods
include the Microsoft HoloLens.RTM., the HTC Vive.RTM., the Oculus
Rift.RTM., the OSVR HDK 1.4.RTM., the PlayStation VR.RTM., the
Epson Moverio BT-300 Smart Glasses.RTM., the Meta 2.RTM., and the
Osterhout Design Group (ODG) R-7 Smartglasses System.RTM.. Numerous
other examples could be listed here as well.
[0134] E. Example Camera-Assembly Rigs
[0135] 1. Rig Having Mounted Camera Assemblies
[0136] In at least one embodiment, the presenter 102 is positioned
in front of a camera-assembly rig, one example of which is shown in
FIG. 9A, which is a front view 900 of an example camera-assembly
rig 902 having mounted thereon four example camera assemblies 924L
("left"), 924R ("right"), 924TC ("top center"), and 924BC ("bottom
center"). Although four mounted camera assemblies, it will be
appreciated by one of skill in the art that different arrangements
are possible and four is merely an example. The camera assemblies
can also be independently configured with many camera
assemblies,
[0137] For the left-right convention that is employed herein,
camera assembly 924L is considered to be "left" rather than "right"
because it is positioned to capture the left side of the presenter
102 if they were standing square to the camera-assembly rig 902
such that it appeared to the presenter 102 substantially the way it
appears in FIG. 9A. Herein, the "L" elements also appear to the
left of the "R" elements when viewing the drawings as they are.
[0138] The camera-assembly rig 902 includes a base 904; vertical
supports 906, 908T ("top"), 908B ("bottom"), and 910; horizontal
supports 912L, 912C ("center"), 912R, 914L, and 914R; diagonal
supports 916T, 916B, 918T, 918B, 920, and 922. The structure and
arrangement that is shown in FIG. 9A is presented for illustration
and not by way of limitation. Other camera-assembly-rig structures
and numbers and positions of camera assemblies are possible in
various different embodiments. For example, in one embodiment, as
will be appreciated by one of skill in the art, each or certain
ones of each camera-assembly could be doubled, tripled or the like.
Another structure and (in that case a three-camera-assembly)
arrangement is depicted in and described below in connection with
FIGS. 10A-D.
[0139] Consistent with the groups-of-elements numbering convention
that is explained above in connection with the VDCs 106 of FIG. 1,
"the camera assemblies 924" refers to the set of four camera
assemblies {924L, 924R, 924TC, 924BC} that is depicted in FIG. 9A,
and "a camera assembly 924," "one of the camera assemblies 924,"
and/or the like refers to any one member of that set. As one would
expect, a specific reference such as "the camera assembly 924TC"
refers to that particularly referenced camera assembly, though such
a reference may nevertheless be made in a context in which a
particular one of the camera assemblies 924 is offered as an
example to describe aspects common among the camera assemblies 924
and not necessarily to distinguish one from the others. Similarly,
a reference such as "the vertical support 908" refers to both the
vertical supports 908T and 908B. And so on.
[0140] In at least one embodiment, the base 904 is made of a
material (e.g., steel) or combination of materials that is dense
and heavy enough to keep the camera-assembly rig 902 stable and
stationary during use. Furthermore, in at least one embodiment,
each of the supports 906-922 is made of a material (e.g., steel) or
combination of materials that is strong and rigid, such that the
relative positions of the base 904 and the respective camera
assemblies 924 do not change during operation, such that a
characteristic geometry among the camera assemblies 924 that are
mounted on the camera-assembly rig 902 can reliably be used in part
of the data processing described herein.
[0141] In the depicted arrangement, by way of example, the triangle
formed by the horizontal support 912C, the diagonal support 920,
and the diagonal support 922 ("the triangle 912-920-922") is an
equilateral triangle, and each of the six triangles that are formed
among different combinations of the base 904; the vertical supports
906, 908, and 910; the horizontal supports 912 and 914, and the
diagonal supports 916 and 918 is a "3-4-5" right triangle as is
known in the art and in mathematical disciplines such as geometry
and trigonometry. These six triangles are the triangle 904-906-916,
the triangle 904-910-918, the triangle 908-912-916, the triangle
908-912-918, the triangle 908-914-916, and the triangle
908-914-918.
[0142] Further with respect to geometry, FIG. 9B is a front view
930 of the camera-assembly rig 902 and camera assemblies 924 of
FIG. 9A, and depicts those elements with respect to an example
reference set of cartesian-coordinate axes 940, which includes an
x-axis 941, a y-axis 942, and a z-axis 943, in accordance with at
least one embodiment. The selection of cartesian-coordinate axes
and the placement in FIG. 9B of the cartesian-coordinate axes 940
are by way of example and not limitation. Other coordinate systems
could be used to organize 3D space, and certainly other placements
of axes could be chosen other than the arbitrary choice that is
reflected in FIG. 9B. This arbitrary choice, however, is maintained
and remains consistent throughout a number of the ensuing
figures.
[0143] Four different points 980, 982, 984, and 986 in 3D space are
labeled in FIG. 9B. Each one has been chosen to correspond with
what is referred to herein as the "front centroid" (e.g., the
centroid of the front face) of the respective visible-light camera
of a given one of the camera assemblies 924. In this description of
FIG. 9B and of a number of the ensuing figures, a red-green-blue
(RGB) camera is used as an example type of visible-light camera;
this is by way of example and not limitation.
[0144] As is also discussed below in connection with at least FIGS.
11A and 11B, in at least one embodiment, each camera assembly 924
includes an RGB camera that is horizontally and vertically centered
on the front face of the given camera assembly 924. The front
centroid of each such RGB camera is the point at the horizontal and
vertical center of the front face of that RGB camera, and therefore
at the horizontal and vertical center of the respective front face
of the respective camera assembly 924 as well. By convention, for
the cartesian-coordinate axes 940, each of the front centroids 980,
982, 984, and 986 has been chosen to have a z-coordinate (not
explicitly labeled in FIG. 9B) equal to zero, not by way of
limitation.
[0145] The 3D-space point 980 is the front centroid of camera
assembly 924L and is located for the cartesian-coordinate axes 940
at coordinates {x980,y980,0}. The notation used in this description
for that point in that space is xyz940::{x980,y980,0}. The 3D-space
point 982 is the front centroid of the camera assembly 924TC and
has coordinates xyz940::{x982,y982,0}. The 3D-space point 984 is
the front centroid of the camera assembly 924R and has coordinates
xyz940::{x984,y980,0}. The 3D-space point 986 is the front centroid
of the camera assembly 924BC and has coordinates xyz940::{x982,
y986,0}.
[0146] Other 3D-space points could be labeled as well, as these
four are merely examples that illustrate among other things that,
at least for some herein-described data operations, a shared (e.g.,
global, common, reference, etc.) 3D-space-coordinate system is used
across multiple different camera assemblies that each have a
respective different vantage point in that shared
3D-space-coordinate system--e.g., in that shared geometry. In this
description, for at least FIG. 9B, FIG. 9C, and FIG. 9D, that
shared 3D-space-coordinate system is the cartesian-coordinates axes
940 (xyz940). 3D-space-coordinate system xyz1040 (and associated
cartesian-coordinate axes 1040) applies to FIG. 10B and
3D-space-coordinate system xyz1040 and the three-camera-assembly
geometry of example camera-assembly rig 1002 applies to FIG.
10A.
[0147] As shown in FIG. 9B, the 3D-space points 980, 982, 984, and
986 are referred to herein at times as front centroids 980, 982,
984, and 986, and front centroids of the RGB cameras of camera
assemblies 924 and at times as being the respective front centroids
of the respective camera assemblies 924 themselves since, as
described above, they are both.
[0148] One or more of the camera assemblies 924 could be fixed to
the camera-assembly rig 902 in a fixed or removable manner. One or
more of the camera assemblies 924 could be fixed to the
camera-assembly rig 902 at any angle deemed suitable by those of
skill in art. Camera assembly 924TC could be oriented straight
ahead and inclined down at a small angle, while the camera assembly
924BC could be oriented straight ahead and inclined up at a small
angle; furthermore, the camera assemblies 924L and 924R could each
be level and rotated inward toward center, perhaps each by the same
angle. This sort of arrangement is depicted by way of example in
FIG. 9C, which is a top view 960 of the camera-assembly rig 902 and
of three of the four camera assemblies 924.
[0149] Among the elements of the camera-assembly rig 902 that are
depicted in FIG. 9A and FIG. 9B, those that are also depicted in
FIG. 9C are the base 904 (shown in FIG. 9C in dashed-and-dotted
outline) and the horizontal supports 912L, 912C, and 912R. The
three camera assemblies that are depicted in FIG. 9C are the camera
assemblies 924L, 924TC, and 924R, each of which is shown with a
dotted pattern representing its respective top surface. Also
carried over from FIG. 9B to FIG. 9C are the cartesian-coordinates
axes 940 (shown rotated consistent with FIG. 9B being a front view
and FIG. 9C being a top view of the camera-assembly rig 902), the
front centroid 980 (having x-coordinate x980) of the camera
assembly 924L, the front centroid 982 (having x-coordinate x982) of
the camera assembly 924TC, and the front centroid 984 (having
x-coordinate x984) of the camera assembly 924R.
[0150] FIG. 9C (as compared with FIGS. 9A and 9B) are three
horizontal supports 962L, 962TC, and 962R. Each horizontal support
962 lies in an xz-plane (has a constant y-value) of the
cartesian-coordinate axes 940 in an orientation that is normal to
the aforementioned horizontal supports 912 and 914. The horizontal
support 962L is connected between the camera assembly 924L and the
horizontal support 912L. The horizontal support 962TC is connected
between the camera assembly 924TC and a junction between the
diagonal supports 920 and 922. The horizontal support 962R is
connected between the camera assembly 924R and the horizontal
support 912R.
[0151] FIG. 9C illustrates camera assemblies 924L and 924R turned
inwards by an angle of 45.degree.. A 45.degree. angle 972 is formed
between the x-axis 941 and a ray 966 normal to the front face of
the camera assembly 924L emanates from front centroid 980. A
45.degree. angle 974 is formed between the x-axis 941 and a ray 968
normal to the front face of the camera assembly 924R emanates from
the front centroid 984. Also depicted is a ray 964 normal to the
front face of the camera assembly 924TC emanates from the front
centroid 982. And though it is not required in this example, the
rays 964, 966, and 968 all intersect at a focal point 970, which
has coordinates xyz940::{x982,y980,z970}.
[0152] The camera-assembly rig 902 and the camera assemblies 924
affixed thereon are in connection with a single reference set of
cartesian-coordinate axes 940. Camera-assembly-specific sets of
cartesian-coordinate axes for camera assemblies 924 are also
possible. Also, transforms between (i) locations in a given 3D
space are possible with respect to the reference
cartesian-coordinate axes 940 and (ii) those same locations in 3D
space with respect to a set of cartesian-coordinate axes oriented
with respect to a given one of the camera assemblies 924.
[0153] Some example camera-assembly-specific sets of
cartesian-coordinate axes are shown in FIG. 9D, which is similar to
FIG. 9B. In particular, FIG. 9D is a partial front view 990 of the
camera-assembly rig 902 and camera assemblies 924, in accordance
with at least one embodiment. In FIG. 9D, none of the individual
components of the camera-assembly rig 902 (e.g., the base 904) are
expressly labeled. Many of the lines of the camera-assembly rig 902
have been reduced to dashed lines and partially redacted in length
so as not to obscure the presentation of the more salient aspects
of FIG. 9D. Also, the lines that form the camera assemblies 924
themselves have been converted to being dashed lines.
[0154] In FIG. 9D, as is the case in FIG. 9B, the camera-assembly
rig 902 is depicted for the reference cartesian-coordinate axes
940. In FIG. 9D, however, each of the camera assemblies 924 is also
depicted with respect to its own example camera-assembly-specific
set of cartesian-coordinate axes 994, each of which is indicated as
having a respective a-axis, a respective b-axis, and a respective
c-axis. Thus, the camera assembly 924L is shown with respect to
cartesian-coordinate axes 994L, the camera assembly 924R with
respect to cartesian-coordinate axes 994R, the camera assembly
924TC with respect to cartesian-coordinate axes 994TC, and the
camera assembly 924BC with respect to cartesian-coordinate axes
994BC.
[0155] In the geometry herein, the a-axis, b-axis, and c-axis of
each camera-assembly-specific cartesian-coordinate axes 994 are not
respectively parallel to the x-axis 941, the y-axis 942, and the
z-axis 943 of the reference cartesian-coordinate axes 940. Rather,
the xy-plane where z=0 of each set of axes 994 is flush with the
respective front face of the corresponding respective camera
assembly 924. The particular angles at which the various camera
assemblies 924 are affixed to the camera-assembly rig 902 with
respect to the reference cartesian-coordinate axes 940 are
therefore relevant to building proper respective transforms between
each of the coordinate axes 994 and the reference axes 940. It is
acknowledged that "axes" is at times used as a singular noun in
this written description is basically as shorthand for "set of
axes" (e.g., "The axes 994 is oriented . . . .").
[0156] Each of the axes 994 inherently has an origin--e.g., a point
having the coordinates {a=0, b=0, c=0} in its respective coordinate
system. With each of the camera assemblies 924 being rigidly
affixed to the camera-assembly rig 902, the location of each of
those origin points has coordinates in the reference axes 940. A
camera-assembly-specific set of cartesian-coordinate axes 994
herein is "anchored" at its corresponding coordinates in the
reference axes 940.
[0157] The camera-assembly-specific set of cartesian-coordinate
axes 994L is anchored at the front centroid 980 of the camera
assembly 924L and is located at
xyz.sub.940::{x.sub.980,y.sub.980,0}; the camera-assembly-specific
set of cartesian-coordinate axes 994R is anchored at the front
centroid 984 of the camera assembly 924R and is located at
xyz.sub.940::{x.sub.984,y.sub.980,0}; the camera-assembly-specific
set of cartesian-coordinate axes 994TC is anchored at the front
centroid 982 of the camera assembly 924TC and is located at
xyz.sub.940::{x.sub.982,y.sub.980,0}; and the
camera-assembly-specific set of cartesian-coordinate axes 994BC is
anchored at the front centroid 986 of the camera assembly 924BC and
is therefore located at xyz.sub.940::{x.sub.982,y.sub.986,0}.
[0158] 2. Rig Having Multi-Camera Mounted Camera Assemblies
[0159] Multi-camera assemblies are included in this disclosure, and
one of skill in the art will appreciate with the benefit of this
disclosure that seven, eight and more cameras are a function of
geometrical space and bandwidth of transmission. For purposes of
simplicity of explanation, a three-camera-assembly arrangement and
associated geometry is depicted in and described below in
connection with FIGS. 10A-10D. In FIGS. 10A-10D, many of the
elements that are similar to corresponding elements that are
numbered in the 900 series in FIGS. 9A-9D are numbered in the 1000
series in FIGS. 10A-10D. FIGS. 9A-9D and 10A-10D are similar and
the differences that are described below.
[0160] FIG. 10A is a first front view 1000 of an example
camera-assembly rig 1002 having mounted thereon three example
camera assemblies 1024L, 1024C, and 1024R, in accordance with at
least one embodiment. In comparing FIG. 10A to FIG. 9A, the camera
assemblies 924TC and 924BC have been removed and replaced by a
single camera assembly 1024C that is situated at the same height
(y-value) as the camera assemblies 1024L and 1024R. Consistent with
that change, there are no supports in FIG. 10A that correspond with
the diagonal supports 920 and 922 of FIG. 9A; instead of horizontal
supports 912L, 912C, and 912R, FIG. 10A has instead just the pair
of horizontal supports 1012L and 1012R.
[0161] FIG. 10B is a second front view 1030 of the camera-assembly
rig 1002 and the camera assemblies 1024 of FIG. 10A, shown with
respect to the above-mentioned example reference set of
cartesian-coordinate axes 1040, in accordance with at least one
embodiment. FIG. 10B is similar in many ways to both FIG. 9B and to
FIG. 10A. The axes 1040 includes an x-axis 1041, a y-axis 1042, and
a z-axis 1043. It can be seen in FIG. 10B that the camera assembly
1024L has a front centroid 1080 having coordinates
xyz.sub.1040::{x.sub.1080,y.sub.1080,0}. The camera assembly 1024C
has a front centroid 1082 having coordinates
xyz.sub.1040::{x.sub.1082,y.sub.1082,0}. Finally, the camera
assembly 1024R has a front centroid 1084 having coordinates
xyz.sub.1040::{x.sub.1084,y.sub.1084,0}.
[0162] FIG. 10C is a partial top view 1060 of the camera-assembly
rig 1002 and camera assemblies 1024 of FIGS. 10A and 10 ft shown
with respect to the reference set of cartesian-coordinate axes 1040
of FIG. 10B. FIG. 10C is nearly identical to FIG. 9C, and in fact
the 3D-space points 970 and 1070 would be the same point in space
(assuming complete alignment of the respective x-axes, y-axes, and
z-axes of the sets of coordinates axes 940 and 1040, as well as
identical dimensions of the respective camera-assembly rigs and
camera assemblies).
[0163] One subtle difference is that the ray 964 is slightly longer
than the ray 1064 due to the elevated position of the camera
assembly 924TC as compared with the camera assembly 1024C. In other
words, the vantage point of the camera assembly 924TC is looking
downward at the focal point 970 whereas the vantage point of the
camera assembly 1024C is looking straight ahead at the focal point
1070. This difference is not explicitly represented in FIGS. 9C and
10C themselves, but rather is inferable from the sets of drawings
taken together.
[0164] FIG. 10D is a partial front view 1090 of the camera-assembly
rig 1002 and camera assemblies 1024, shown with respect to the
reference set of cartesian-coordinate axes 1040, in which each
camera assembly 1024 is also shown with respect to its own example
camera-assembly-specific set of cartesian-coordinate axes, in
accordance with at least one embodiment. FIG. 10D is nearly
identical in substance to FIG. 9D (excepting of course that they
depict different embodiments), and therefore is not covered in
detail here. Note that the camera-assembly-specific axes 1094L is
anchored at the front centroid 1080 of the camera assembly 1024L,
the camera-assembly-specific axes 1094C is anchored at the front
centroid 1082 of the camera assembly 1024C, and the
camera-assembly-specific axes 1094R is anchored at the front
centroid 1084 of the camera assembly 1024R. Unlike in FIG. 9D,
there is in FIG. 10D a set of camera-assembly specific axes (in
particular the axes 1094C) that are respectively parallel to the
reference set of axes (which in the case of FIG. 10D is the axes
1040).
[0165] F. Example Camera Assembly
[0166] An example camera assembly is shown in further detail in
FIGS. 11A-11C. And although FIG. 11A is a front view 1100 of the
above-mentioned camera assembly 1024L in accordance with at least
one embodiment, it should be understood that any one or more of the
VDCs 106, any one or more of the camera assemblies 924, and/or any
one or more of the camera assemblies 1024 could have a structure
similar to the camera-assembly structure (e.g., composition,
arrangement, and/or the like) that is depicted in and described in
connection with FIGS. 11A-11C.
[0167] As can be seen in FIG. 11A, the camera assembly 1024L
includes an RGB camera 1102, an IR camera 1104L, an IR camera
1104R, and an IR illuminator 1106. And certainly other suitable
components and arrangements of components could be used. In at
least one embodiment, one or more of the camera assemblies 924
and/or 1024 is a RealSense 410.RTM. from Intel Corporation of Santa
Clara, Calif. In at least one embodiment, one or more of the camera
assemblies 924 and/or 1024 is a RealSense 430.RTM. from Intel
Corporation. And certainly other examples could be listed as
well.
[0168] The RGB camera 1102 of a given camera assembly 1024 could be
any RGB (or other visible-light) video camera deemed suitable by
those of skill in the art for a given implementation. The RGB
camera 1102 could be a standalone device, a modular component
installed in another device (e.g., in a camera assembly 1024), or
another possibility deemed suitable by those of skill in the art
for a given implementation. In at least one embodiment, the RGB
camera 1102 includes (i) a color sensor known as the Chameleon3 3.2
megapixel (MP) Color USB3 Vision (a.k.a. the Sony IMX265)
manufactured by FLIR Integrated Imaging Solutions Inc. (formerly
Point Grey Research), which has its main office in Richmond,
British Columbia, Canada and (ii) a high-field-of-view,
low-distortion lens. As described herein, some embodiments involve
the camera assemblies 1024 using their respective RGB cameras 1102
to gather video of the subject (e.g., the presenter 102) and to
transmit a raw video stream 208 of the subject to a server such as
PSS 202.
[0169] Each IR camera 1104 of a given camera assembly 1024 could be
any IR camera deemed suitable by those of skill in the art for a
given implementation. Each IR camera 1104 could be a standalone
device, a modular component installed in another device (e.g., in a
camera assembly 1024), or another possibility deemed suitable by
those of skill in the art for a given implementation. In at least
one embodiment, each IR camera 1104 includes (i) a
high-field-of-view lens and (ii) an IR sensor known as the OV9715
from OmniVision Technologies, Inc., which has its corporate
headquarters in Santa Clara, Calif. As described herein, some
embodiments involve the various camera assemblies 1024 using their
respective pairs of IR cameras 1104 to gather depth data of the
subject (e.g., the presenter 102) and to transmit a depth-data
stream 110 of the subject to a server such as PSS 202.
[0170] The IR illuminator 1106 of a given camera assembly 1024
could be any IR illuminator, emitter, transmitter, and/or the like
deemed suitable by those of skill in the art for a given
implementation. The IR illuminator 1106 could be a set of one or
more components that alone or together carry out the
herein-described functions of the IR illuminator 1106. For example,
IR illuminator 1106 could include LIMA high-contrast IR dot
projector from Heptagon, Large Divergence 945 nanometer (nm)
vertical-cavity surface-emitting laser (VCSEL) Array Module from
Princeton Optronics as will be appreciated by one of skill in the
art.
[0171] In at least one embodiment, to aid in gathering (e.g.,
obtaining, generating, and/or the like) depth data, depth images,
3D meshes, and the like, the IR illuminator 1106 of a given camera
assembly 1024 is used to project a pattern of IR light on the
subject. The IR cameras 1104L and 1104R may then be used to gather
reflective images of this projected pattern, where such reflective
images can then be stereoscopically compared and analyzed to
ascertain depth information regarding the subject. As mentioned,
stereoscopic analysis of projected-IR-pattern reflections is but
one way that such depth information could be ascertained, and those
of skill in the art may select another depth-information-gathering
technology without departing from the scope and spirit of the
present disclosure.
[0172] FIG. 11B is a front view 1120 of the camera assembly 1024L
shown with respect to an example portion of the
cartesian-coordinate axes 1040, in accordance with at least one
embodiment. The x-axis 1041 and the y-axis 1042 are shown in FIG.
11B, though the z-axis 1043 is not (although the different points
that are labeled in FIG. 11B would in fact have different z-values
than one another, due to the orientation of the camera assembly
1024L as depicted in FIG. 10C). Also depicted is the 3D-space point
1080, which, as can be seen in FIG. 11B and as was mentioned above,
is the front centroid of both the camera assembly 1024L as a whole
and of the RGB camera 1102 of the camera assembly 1024L. As was
described above, the front centroid 1080 has coordinates
xyz.sub.1040::{x.sub.1080,y.sub.1080,0}. The IR camera 1104L has a
front centroid 1124L having an x-coordinate of x1124L, a
y-coordinate of y1080, and a non-depicted z-coordinate. The IR
camera 1104R has a front centroid 1124R having an x-coordinate of
x1124R, a y-coordinate of y1080, and a non-depicted z-coordinate.
The IR illuminator 1106 has a front centroid 1126 having an
x-coordinate of x1126, a y-coordinate of y1080, and a non-depicted
z-coordinate.
[0173] FIG. 11C is a modified virtual front view 1140 of the camera
assembly 1024L, also shown with respect to the portion from FIG.
11B of the cartesian-coordinate axes 1040. FIG. 11C is
substantially identical to FIG. 11B, other than that the RGB camera
1102 has been replaced by a virtual depth camera 1144L at the exact
same position having a virtual front centroid 1080 that is
co-located with the front centroid 1080 of the RGB camera 1102 of
the camera assembly 1024L at the coordinates
xyz.sub.1040::{x.sub.1080,y.sub.1080,0}.
[0174] The relevance of the virtual depth camera 1144L being at the
same location of the actual RGB camera 1102 of the camera assembly
1024L is explained more fully below. And each of the other camera
assemblies 924 and 1024 could similarly be considered to have a
virtual depth camera 1144 co-located with their respective RGB
camera 1102. In particular with respect to the camera assemblies
1024C and 1024R, in the described embodiment, the camera assembly
1024C is considered to have a virtual depth camera 1144C co-located
(e.g., having a common front centroid 1082) with the respective RGB
camera 1102 of the camera assembly 1024C, and the camera assembly
1024R is considered to have a virtual depth camera 1144R co-located
(e.g., having a common front centroid 1084) with the respective RGB
camera 1102 of the camera assembly 1024R. And certainly other
example arrangements could be used as well.
III. Example Scenarios
[0175] A. Example Presenter Scenarios
[0176] One possible setup in which the presenter 102 may be
situated is depicted in FIG. 12, which is a diagram of a first
example presenter scenario 1200 in which the presenter 102 is
positioned in an example room 1202 in front of the camera-assembly
rig 1002 and the camera assemblies 1024 of FIG. 10A, in accordance
with at least one embodiment. The presenter scenario 1200 takes
place in the room 1202, which has a floor 1204, a left wall 1206,
and a back wall 1208. Clearly the room 1202 could--and likely
would--have other walls, a ceiling, etc., as only an illustrative
part of the room 1202 is depicted in FIG. 12.
[0177] The view of FIG. 12 is from behind the presenter 102, and
therefore a back 1028 of the presenter 102 is shown as actually
positioned where the presenter 102 would be standing in this
example. It can be seen that the back 1028 of the presenter 102 is
shown with a pattern of diagonal parallel lines that go from
lower-left to upper-right. Also depicted in FIG. 12 is a front 102F
of the presenter 102, using a crisscross pattern formed by diagonal
lines. Clearly the presenter would not appear floating in two
places to an observer standing behind the presenter 102. The
depiction of the front 102F of the presenter 102 is provided merely
to illustrate to the reader of this disclosure that a remote viewer
would generally see the front 102F of the presenter 102. It is
further noted that the crisscross pattern that is depicted on the
front 102F of the presenter 102 in FIG. 12 is consistent with the
manner in which the presenter persona 116 is depicted in FIGS. 1,
2, 14, 15, and 24-29, as examples.
[0178] Also depicted as being in the room 1202 in FIG. 12 is PSS
202, which in this case is embodied in the form of a desktop
computer that has a wireless (e.g., Wi-Fi) data connection 1210
with the camera rig 1002 and a wired (e.g., Ethernet) connection
1212 to a data port 1214, which may in turn provide high-speed
Internet access, as an example, as direct high-speed data
connections to one or more viewer locations are contemplated as
well. The wireless connection 1210 could be between PSS 202 and a
single module (not depicted) on the camera rig 1002, where that
single module in turn interfaces with each of the camera assemblies
1024. In another embodiment, there is an independent wireless
connection 1210 (1210L, 1210R, and 1210C) with each of the
respective camera assemblies 1024. And certainly other possible
arrangements could be described here as well.
[0179] As described earlier, in one example, the presenter 102 is
delivering an astronomy lecture in a lecture hall. Such an example
is depicted in FIG. 13, which is a diagram of a second example
presenter scenario 1300 in which the presenter 102 is positioned on
an example stage 1302 in front of the camera-assembly rig 1002 and
the camera assemblies 1024 of FIG. 10A, in accordance with at least
one embodiment. Some aspects of FIG. 13 that are identical or at
least quite similar to parallel aspects in FIG. 12, and thus that
are not further described here, include the presenter 102, the
front 102F of the presenter 102, the back 1028 of the presenter
102, the camera rig 1002, the camera assemblies 1024 (not
specifically enumerated in FIG. 13), PSS 202, a wireless connection
1310, a wired connection 1312, and a data port 1314.
[0180] As can be seen in FIG. 13, the stage 1302 has a surface 1302
and a side wall 1306. The camera rig 1002 is positioned at the
front of the stage 1302 on the surface 1304. The presenter 102 is
standing on the surface 1304, facing the camera rig 1002, and
addressing a live, in-person audience 1308. Certainly other
arrangements could be depicted, as the scenarios 1200 and 1300 are
provided by way of example. Also, as is the case with FIG. 12, the
front 102F of the presenter 102 is included in FIG. 13 to show what
the audience 1308 would be seeing, and not at all to indicate that
somehow both the front 102F and the back 1028 of the presenter 102
would be visible from the overall perspective of FIG. 13.
[0181] B. Example Viewer Scenarios
[0182] 1. Virtual Reality (VR)
[0183] As mentioned above, there are several ways in which a viewer
could experience the presentation by the presenter 102. Some
examples include VR experiences and AR experiences. One example VR
scenario is depicted in FIG. 14, which is a diagram of a first
example viewer scenario 1400 according to which a viewer is using
HMD 112 to view the 3D presenter persona 116, in accordance with at
least one embodiment. The scenario 1400 is quite simplified, but in
general is included to demonstrate the point that the viewer could
view the presentation in a VR experience.
[0184] As can be seen in FIG. 14, in the scenario 1400, the viewer
sees a depiction 1402 on the display 114 of HMD 112. In the
depiction 1402, the 3D presenter persona 116 is depicted as
standing on a (virtual) lunar surface 1404 with a (virtual)
starfield (e.g., the lunar sky) 1406 as a backdrop. A (virtual)
horizon 1408 separates the lunar surface 1404 from the starfield
1406. It will be quite apparent to those of skill in the art and to
people in general that the number of possible VR examples that
could be used in various different implementations is as limitless
as the human imagination.
[0185] 2. Augmented Reality (AR)
[0186] Another type of viewer scenario, in this case an AR viewer
scenario, is depicted in FIG. 15, which is a diagram of a second
example viewer scenario 1500, according to which a viewer is using
HMD 112 to view the 3D persona 116 of the presenter 102 as part of
an example AR experience, in accordance with at least one
embodiment. The scenario 1500 is quite simplified as well, and is
included to demonstrate that the viewer could view the presentation
in an AR experience.
[0187] In the particular example that is shown in FIG. 15, there is
a depiction 1502 in which the only virtual element is the 3D
presenter persona 116. Certainly one or more additional virtual
elements could be depicted in various different embodiments. In
this example, then, the 3D presenter persona 116 is depicted as
standing on the (real) ground 1504 in front of some (real) trees
1510 and some (real) clouds 1508 against the backdrop of the (real)
sky 1506. In this simple example, the viewer has chosen to view the
lecture by the presenter 102 from a location out in nature, but of
course this is presented merely by way of example and not
limitation.
IV. Example Operation
[0188] A. Example Sender-Side Operation
[0189] 1. Introduction
[0190] FIG. 16A is a flowchart of a first example method 1600, in
accordance with at least one embodiment. In various different
embodiments, the method 1600 could be carried out by any one of a
number of different entities--or perhaps by a combination of
multiple such entities. Some examples of disclosed entities and
combinations of disclosed entities that could carry out the method
1600 include the VDCs 106, PSS 202, and PSS 502. By way of example
and not limitation, the method 1600 is described below as being
carried out by PSS 202.
[0191] Furthermore, the below description of the method 1600 is
given with respect to other elements that are also in the drawings,
though this again is for clarity of presentation and by way of
example, and in no way implies limitation. Each step 1602-1610 is
described in a way that refers by way of example to various
elements in the drawings of the present disclosure. In particular,
and with some exceptions, the method 1600 is generally described
with respect to the presenter scenario 1300, the viewer scenario
1400, the camera-assembly rig 1002, the camera assemblies 1024L,
1024R, and 1024C, and the basic information flow of FIG. 2 (albeit
with the camera assemblies 1024LCR taking the respective places of
the VDCs 206LCR).
[0192] 2. Receiving Raw Video Streams from Camera Assemblies
[0193] At step 1602, PSS 202 receives three (in general M, where M
is an integer) video streams 208 including the raw video streams
208L, 208C, and 208R, collectively the raw video streams 208LCR,
respectively captured of the presenter 102 by the respective RGB
video cameras 1102 of the camera assemblies 1024. RGB video cameras
1102 of the respective camera assemblies 1024 capture video, and,
specifically, PSS 202 receives raw video streams 208 from the
respective camera assemblies 1024. A similar convention is employed
for depth-data streams 210.
[0194] As described herein, each video stream 208 includes video
frames that are time-synchronized with the video frames of each of
the other such video streams 208 according to a shared frame rate.
That is, in accordance with embodiments of the present systems and
methods, not only do multiple entities (e.g., the camera assemblies
1024) and the corresponding data (e.g., the raw video streams 208)
that those entities process (e.g., receive, generate, modify,
transmit, and/or the like) operate according to (or at least
reflect) a shared frame rate, they do so in a time-synchronized
manner.
[0195] Of course certain corrections and synchronization steps may
be taken in various embodiments using hardware, firmware, and/or
software to achieve or at least very closely approach
time-synchronized operation, but the point is this: not only does a
given frame x (e.g., the frame having sequence number x, frame
number x, timestamp x, and/or other data x useful in
synchronization of video frames with one another) in one data
stream 208 have the same duration as frame x in each of the other
corresponding data streams 208, but each frame x would start and
therefore end at the same time, at least within an acceptable
margin of error that may differ among various implementations.
[0196] In at least one embodiment, the shared frame rate is 120
frames per second (fps), which would make the shared-frame-rate
period 1/120 of a second (81/3 ms). In at leas one embodiment, the
shared frame rate is 240 fps, which would make the
shared-frame-rate period 1/240 of a second (41/6 ms). In at least
one embodiment, the shared frame rate is 300 fps, which would make
the shared-frame-rate period 1/300 of a second (31/3 ms). In at
least one embodiment, the shared frame rate is 55 fps, which would
make the shared-frame-rate period 1/55 of a second (18 2/11 ms).
And certainly other frame rates and corresponding periods could be
used in various different embodiments, as deemed suitable by those
of skill in the art for a given implementation.
[0197] Further, as described above, each of the video cameras 1102
has a known vantage point in a predetermined coordinate system, in
this case the predetermined coordinate axes 1040. In particular, as
explained above, the known vantage point of the video camera 1102
of the camera assembly 1024L is at their common front centroid
1080, oriented towards the 3D-space point 1070; the known vantage
point of the video camera 1102 of the camera assembly 1024C is at
their common front centroid 1082, also oriented towards the
3D-space point 1070; and the known vantage point of the video
camera 1102 of the camera assembly 1024R is at their common front
centroid 1084, also oriented towards the 3D-space point 1070. As
explained, all of the points 1070, 1080, 1082, and 1084 are in the
predetermined coordinate system 1040. Due to their co-location and
static arrangement during operation, the various front centroids
1080, 1082, and 1084 are referred to at times in this written
description as the vantage points 1080, 1082, and 1084,
respectively.
[0198] 3. Generation of 3D Mesh of Subject
[0199] a. Receipt of Depth Images from Camera Assemblies
[0200] At step 1604, PSS 202 obtains 3D meshes of the presenter 102
at the shared frame rate, and such 3D meshes are time-synchronized
with the video frames of each of the 3 raw video streams 208 such
that 3D mesh x is time-synchronized with frame x in each raw video
stream 208. PSS 202 obtains or generates at least one 3D mesh of
the presenter 102. In one embodiment, PSS at least one pre-existing
mesh is available to PSS 202.
[0201] Although PSS 202 could carry out step 1604 in a number of
different ways, examples of which are described herein, in this
particular example, step 1604 includes PSS 202, receiving from the
camera assemblies 1024, depth-data streams 210 made up of depth
images generated by the respective camera assemblies 1024.
[0202] In this example, those depth images are generated by the
camera assemblies 1024 in the following manner: each camera
assembly 1024 uses its respective IR illuminator 1106 to project a
non-repeating, pseudorandom temporally static pattern of IR light
on to the presenter 102 and further uses its respective IR cameras
1104L and 1104R to gather two different reflections of that pattern
(reflections of that pattern from two different vantage
points--e.g., the front centroids 1124L and 1124R of the camera
assembly 1024L) off of the presenter 102. Each camera assembly 1024
conducts hardware-based stereoscopic analysis to determine a depth
value for each pixel location in the corresponding depth image,
where such pixel locations in at least one embodiment correspond on
a one-to-one basis with color pixels in the video frames in the
corresponding raw video stream 208 from the same camera assembly
1024. The non-repeating nature of the IR pattern could be globally
non-repeating or locally non-repeating to various extents in
various different embodiments.
[0203] Thus, in at least one embodiment, when carrying out step
1604, PSS 202 receives a depth image from each camera assembly 1024
for each shared-frame-rate time period. This provides PSS 202 with,
in this example, three depth images of the presenter 102 for each
frame (e.g., for each shared-frame-rate time period). In at least
one embodiment, each of those depth images will be made up of depth
values (e.g., depth pixels) that each represent a distance from the
respective vantage point of the camera assembly from which the
corresponding depth frame was received.
[0204] b. Projection of Received Depth Images Onto Shared Geometry
in Construction of Single 3D-Point Cloud of Subject
[0205] PSS 202 can use the known location of the vantage point of
that camera assembly 1024 in the predetermined coordinate system
1040 to convert each such distance to a point (having a 3D-space
location) in that shared geometry 1040. (Note that "the axes 1040,"
"the coordinate axes 1040," "the predetermined coordinate system
1040," "the shared geometry 1040," and the like are all used
interchangeably herein.) PSS 202 then combines all such identified
points into a single 3D-point cloud that is representative of the
subject (e.g., the presenter 102).
[0206] In at least one embodiment, and using the camera assembly
1024C by way of example, to convert (i) a measured distance from
the vantage point of the camera assembly 1024L as reflected in a
depth-pixel value of a depth pixel in a depth frame that is
received by PSS 202 from the camera assembly 1024C into (ii) a 3D
point location in the shared geometry 1040, PSS 202 may carry out a
series of calculations, transformations, and the like. An example
of such processing is described in the ensuing paragraphs in
connection with FIGS. 17-19.
[0207] FIG. 17 is a perspective diagram depicting a view 1700 of a
first example projection from a focal point 1712 of the camera
assembly 1024C (as an example one of the camera assemblies 1024)
through the four corners of a 2D pixel array 1702 of the example
camera assembly 1024C on to the shared geometry 1040, in accordance
with at least one embodiment. As to the type of processing in
general that is described here, the focal point 1712 and the pixel
array 1702 could correspond to one of three different vantage
points on the camera assembly 1024C, namely (i) the vantage point
1124L of the IR camera 1104L of the camera assembly 1024C, (ii) the
vantage point 1124R of the IR camera 1104R of the camera assembly
1024C, or (iii) the vantage point 1082 of the virtual depth camera
1144C--and of the RGB camera 1102--of the camera assembly 1024C.
Note that the focal point 1712 is different from the vantage point
in all these cases, but they are optically associated with one
another as known in the art.
[0208] In this example description, PSS 202 conducts processing on
depth frames received in the depth-data stream 210C from the camera
assembly 1024C. In one example, focal point 1712 and the pixel
array 1702 are associated with the third option outlined in the
preceding paragraph--the focal point 1712 and the pixel array 1702
are associated with the vantage point 1082 of the virtual depth
camera 1144C--and of the RGB camera 1102--of the camera assembly
1024C.
[0209] Referring to FIG. 17, the pixel array 1702 is framed on its
bottom and left edges by a 2D set of coordinate axes 1706 that
includes a horizontal a-axis 1708 and a vertical b-axis 1710.
Emanating from the focal point 1712 through the top-left corner of
the pixel array 1702 is a ray 1714, which continues on and projects
to the top-left corner of an xy-plane 1704 in the shared geometry
1040. For the convenience of the reader, the ray 1714 is depicted
as a dotted line between the focal point 1712 and its crossing of
the (ab) plane of the pixel array 1702 and is depicted as a dashed
line between the plane of the pixel array 1702 and the xy-plane
1704. This convention is used to show the crossing point of a given
ray with respect to the plane of the pixel array 1702, and is
followed with respect to the other three rays 1716, 1718, and 1720
in FIG. 17, and with respect to the rays that are shown in FIGS. 18
and 19 as well.
[0210] The xy-plane 1704 sits at the positive depth z1704 in the
shared geometry 1040; as the reader can see, the view in FIGS.
17-19 of the shared geometry 1040 is from the perspective of the
camera assembly 1024C, and thus is rotated 180.degree. around the
y-axis 1042 as compared with the view that is presented in FIG. 10B
and others. Also emanating from the focal point 1712 are (i) a ray
1716, which passes through the top-right corner of the pixel array
1702 and projects to the top-right corner of the xy-plane 1704,
(ii) a ray 1718, which passes through the bottom-right corner of
the pixel array 1702 and projects to the bottom-right corner of the
xy-plane 1704, and (iii) a ray 1720, which passes through the
bottom-left corner of the pixel array 1702 and projects to the
bottom-left corner of the xy-plane 1704.
[0211] Thus, the view 1700 of FIG. 17 illustrates--as a general
matter and in at least one example arrangement--the interrelation
(for a given camera or virtual camera) of the focal point 1712
(which could pertain to color pixels and/or depth pixels), the 2D
pixel array 1702 (of color pixels or depth pixels, or combined
color-and-depth pixels, as the case may be, and the projection from
the focal point 1712 via that 2D pixel array 1702 on to a shared 3D
real-world geometry.
[0212] The xy-plane 1704 is included in this disclosure to show the
scale and projection relationships between the 2D pixel array 1702
and the 3D shared geometry 1040. A subject--such as the presenter
102--would not need to be situated perfectly in the xy-plane 1704
to be seen by the camera assembly 1024C; rather, the xy-plane 1704
is presented to show that a point that is detected to be at the
depth z1704 could be thought of as sitting in a 2D plane 1704 in
the real world that corresponds to some extent with the 2D pixel
array 1702 of the camera assembly 1024C. The depicted xy-plane 1704
(and other types of planes) could have been depicted in FIG. 17. In
shared geometry 1040, every point in the shared geometry has a
z-value and resides in a particular xy-plane (at a particular
x-coordinate and y-coordinate on that particular xy-plane).
[0213] FIG. 18 is a perspective diagram depicting a view 1800 of a
second example projection from the focal point 1712 (of the virtual
depth camera 1144L of the camera assembly 1024C) through a
pixel-array centroid 1802 of the 2D pixel array 1702 (of the
virtual depth camera 1144L) on to the shared geometry 1040, in
accordance with at least one embodiment. As mentioned herein, the
virtual depth camera 1144C of the camera assembly 1024C has a front
centroid 1082 having coordinates
xyz.sub.1040::{x.sub.1082,y.sub.1080,0}. In this described example,
the pixel-array centroid 1802 of the 2D pixel array 1702
corresponds with the front centroid 1082 of the camera assembly
1024C. The pixel array 1702 may have an even number of pixels in
each row and column, and may therefore not have a true center
pixel, so the pixel-array centroid 1802 may or may not represent a
particular pixel.
[0214] Many aspects of FIG. 18 are common or at least similar to
FIG. 17, though some aspects of FIG. 17 have been removed for
clarity: for example, FIG. 18 does not explicitly depict rays
emanating from the focal point 1712 and touching each of the four
corners of the pixel array 1702. FIG. 18 includes a ray 1806 that
emanates from the focal point 1712, passes through the pixel-array
centroid 1802, and projects to the above-mentioned 3D point 1070,
which is pictured in FIG. 18 as residing in an xy-plane 1804, which
itself is situated at a positive-z depth of z1070, a value that is
shown in FIG. 10C as well. FIGS. 10C and 18 illustrate that the 3D
point 1070 has coordinates
xyz.sub.1040::{x.sub.1082,y.sub.1080,z.sub.1070}. The depth z1704
that is depicted in FIG. 17 may or may not be the same as the depth
z1070 that is depicted in FIG. 18.
[0215] FIG. 18 illustrates that the pixel-array centroid 1802 has
coordinates {a1802,b1802} in the coordinate system 1706 associated
with the pixel array 1702. In at least one embodiment, the
ab-coordinate system 1706 corresponds with the ab-plane at c=0 of
the camera-assembly-specific coordinate system 1094C as shown in
FIG. 10D. The {a=0, b=0} point in the coordinate system 1094C is
anchored at the front centroid 1082; pixel-array centroid 1802 is
not at the {a=0, b=0} point of the ab-coordinate system 1706 of the
pixel array 1702, illustrating the point that an
a-shift-and-b-shift transform could be needed between the two
coordinate systems in some embodiments (and perhaps a c-shift
transform in others). In some embodiments, the
camera-assembly-specific coordinate system 1094C is selected such
that the a-axis is along the bottom edge and the b-axis is along
the left edge of the virtual depth camera 1144C. And certainly many
other example arrangements could be used as well.
[0216] In embodiments in which the pixel-array centroid 1802
corresponds to an actual pixel in the pixel array 1702, PSS 202
could determine the 3D coordinates of the point 1070 in the shared
geometry 1040 from (i) a depth-pixel value for the pixel-centroid
1802 (in which the depth-pixel value is received in an embodiment
by PSS 202 from the camera assembly 1024C), (ii) data reflecting
the fixed physical relationship between the camera assembly 1024C
and the shared geometry 1040, and (iii) data reflecting the
relationship between the focal point 1712, the pixel array 1702,
and other relevant inherent characteristics of the camera assembly
1024. The second and third of those three categories of data are
referred to as the "extrinsics" and the "intrinsics," respectively,
of the camera assembly 1024C. These terms are further described
herein.
[0217] In the particular arrangement that is depicted in FIG. 18, a
single line can be drawn that intersects the focal point 1712, the
pixel-array centroid 1802, and the point 1070 in the shared
geometry 1040; for any pixel location in the pixel array 1702 other
than the pixel-array centroid 1802, however, there would be a
relevant angle between (i) the ray 1806 that is depicted in FIG. 18
and (ii) a ray emanating from the focal point 1712, passing through
that other pixel location, and projecting somewhere other than the
point 1070 in the shared geometry 1040. That angle would be
relevant in determining the coordinates in the shared geometry 1040
of that other point. Such an example is depicted in FIG. 19, in
fact, which is a perspective diagram depicting a view 1900 of a
third example projection from the focal point 1712 (of the virtual
depth camera 1144C of the camera assembly 1024C) through an example
pixel 1902 (also referred to herein at times as "the pixel location
1902") in the 2D pixel array 1702 on to the shared geometry 1040,
in accordance with at least one embodiment.
[0218] As can be seen in FIG. 19, the pixel 1902 has coordinates
{a1902,b1902} in the ab-coordinate system 1706 of the pixel array
1702. Ray 1904 emanating from the focal point 1712, passes through
the plane of the pixel array 1702 at the pixel location of the
pixel 1902, and projects on to a point 1906 in the shared geometry
1040. The point 1906 is shown by way of example as residing in an
xy-plane 1910 in the shared geometry 1040, where the xy-plane 1910
itself resides at a positive depth z1906. As such, it can be seen
by inspection of FIG. 19 that the example 3D point 1906 has
coordinates xyz.sub.1040::{x.sub.1906,y.sub.1906,z.sub.1906}.
[0219] Unlike a potentially known focal point such as the 3D point
1070 that is described above, PSS 202 in at least one embodiment
has no prior knowledge of what x1906, y1906, or z1906 might be.
Rather, as will be evident to those of skill in the art having the
benefit of this disclosure, PSS 202 will receive from the camera
assembly 1024C a depth value for the pixel 1902, and derive the
coordinates C of the 3D point 1906 from (i) that received depth
value, (ii) the extrinsics of the camera assembly 1024C, and (iii)
the intrinsics of the camera assembly 1024C. In at least one
embodiment, this geometric calculation takes into account an angle
between the ray 1806 of FIG. 18 (as a reference ray) and the ray
1906 of FIG. 19. PSS 202 can determine this angle in realtime, or
be pre-provisioned with respective angles for each respective pixel
location (or perhaps a subset of the pixel locations) in the pixel
array 1702. And certainly other approaches could be listed here as
well.
[0220] Geometric relationships that are depicted in FIGS. 17-19, as
well as the associated mathematical calculations, are useful for
determining 3D coordinates in the shared geometry based on
depth-pixel values received from the camera assemblies 1024.
Certainly this geometry and related mathematics are useful for
that, but they are also useful for determining which pixel location
in a 2D pixel array such as the pixel array 1702 projects to an
already known location in the shared geometry 1040. This
calculation is useful for determining which pixel in a color image
(e.g., a video frame) projects on to a known (e.g., already
determined) location of a vertex in a 3D mesh.
[0221] In other words, given a vertex in the 3D space of the
predetermined coordinate system 1040, the geometry and mathematics
depicted in--and described in connection with--FIGS. 17-19 are used
by PSS 202 to determine which pixel location (and therefore which
pixel and therefore which color (and brightness, and the like)) in
a given 2D video frame projects on to that vertex. This latter type
of calculation can be carried out on the receiver side (e.g., by a
rendering device such as HMD 112), in at least one embodiment, the
color information (e.g., the encoded video streams 218) is
transmitted from PSS 202 to HMD 112 separately from the geometric
information (pertaining to the 3D mesh of the subject, e.g., the
geometric-data stream 220LCR), and it is the task of the rendering
device to integrate the color information with the geometric
information in rendering the viewpoint-adaptive 3D persona 116 of
the presenter 102.
[0222] c. Mesh Extraction from 3D-Point Cloud
[0223] i. Introduction
[0224] Returning to the description of 3D-mesh generation (e.g.,
step 1604 of the method 1600), in at least one embodiment, PSS 202
combines all of the 3D points from all three received depth images
into a single 3D point cloud, which PSS 202 then integrates into
what is known in the art as a "voxel grid," from which PSS 202
extracts--by way of a number of iterative processing steps--what is
known as and referred to herein as a 3D mesh of the subject (e.g.,
the presenter 102).
[0225] In the present disclosure, a 3D mesh of a subject is a data
model (e.g., a collection of particular data arranged in a
particular way) of all or part of the surface of that subject. The
3D-space points that make up the 3D mesh such as the 3D-space
points that survive and/or are identified by the herein-described
mesh-generation processes (e.g., step 1604)--are referred to
interchangeably as "vertices," "mesh vertices," and the like. A
term of art for the herein-described 3D-mesh-generation processes
is "multi-camera 3D reconstruction."
[0226] As a relatively early step in at least one embodiment of the
herein-described 3D-mesh-generation processing, PSS 202 uses one or
more known techniques--e.g., relative locations, clustering,
eliminating outliers, and/or the like--to eliminate points from the
point cloud that are relatively easily determined to not be part of
the presenter 102. In at least one embodiment, the exclusion of
non-presenter points is left to the below-described Truncated
Signed Distance Function (TSDF) processing. Other approaches may be
used as well.
[0227] ii. Identification of Mesh Vertices Using Truncated Signed
Distance Function (TSDF) Processing
[0228] Among the remaining points, PSS 202 may carry out further
processing to identify and eliminate points that are non-surface
(e.g., internal) points of the presenter 102, and perhaps also to
identify and eliminate at least some points that are not part of
(e.g., external to) the presenter 102. In at least one embodiment,
PSS 202 identifies surface points (e.g., vertices) of the presenter
102 using what is known in the art as TSDF processing, which
involves a comparison of what is referred to herein as a
current-data TSDF volume to what is referred to herein as a
reference TSDF volume. The result of that comparison is the set of
vertices of the current 3D mesh of the presenter 102.
[0229] The reference TSDF volume is a set of contiguous 3D spaces
in the shared geometry 1040. Those 3D spaces are referred to herein
as reference voxels, and each has a reference-voxel centroid having
a known location--referred to herein as a "reference-voxel-centroid
location"--in the shared geometry 1040. The current-data TSDF
volume is made up of (e.g., reflects) actual measured 3D-data
points corresponding to the current frame, and in particular
typically includes a respective 3D-data point located (somewhere)
within each of the reference voxels of the reference TSDF volume.
Each such 3D-data point also has a known 3D-data-point location in
the shared geometry 1040.
[0230] Thus, one computation that can be done in advance (or in
realtime) is to compute a respective reference distance between (i)
the vantage point of the corresponding camera and (ii) the known
reference-voxel-centroid location of each reference-voxel centroid.
In the case of the camera assembly 1024L, that vantage point is the
above-identified front centroid 1080. During the realtime TSDF
processing, PSS 202 further computes a respective actual distance
between (i) the vantage point of the corresponding camera and (ii)
the 3D-data point that is located within each reference voxel.
[0231] For each respective reference voxel, PSS 202 in at least one
embodiment next computes the difference between (i) the reference
distance (between the camera vantage point and the reference-voxel
centroid) of that particular reference voxel and (ii) the actual
distance (between the camera vantage point and the 3D-data point
located within the bounds of) that particular reference voxel.
Thus, for a given reference voxel i, a difference .DELTA.i is given
by:
.DELTA..sub.i=ReferenceDistance.sub.i-ActuralDistance.sub.i (Eq.
1)
[0232] Next, in at least one embodiment, for each respective
reference voxel i, PSS 202 computes the quotient (referred to
herein as the "TSDF value") of (i) the computed .DELTA.i for that
reference voxel and (ii) a truncation threshold Ttrunc that is
common to each such division calculation in a given instance of
carrying out TSDF processing. Thus, for a given reference voxel i,
the TSDF value TSDFi is given by:
TSDFi = .DELTA. i Ttrunc .times. .times. Error ! .times. .times.
Bookmark .times. .times. not .times. .times. defined . ( Eq .
.times. 2 ) ##EQU00001##
[0233] Next, in at least one embodiment, for each respective
reference voxel i, PSS 202 carries out computation to compare the
various TSDFi values with various TSDF thresholds (detailed just
below) and further stores data and/or deletes (e.g., removes from a
list or other array or structure) data reflecting that: [0234] (i)
each reference voxel i that has a sufficiently positive TSDF.sub.i
(e.g., a TSDF.sub.i that is greater than a positive TSDF threshold)
is considered to be a reference voxel that does not include a
3D-data point that is any part of the presenter 102 at all; [0235]
(ii) each reference voxel i that has a sufficiently negative
TSDF.sub.i (e.g., a TSDF.sub.i that is less than a negative
TSDF.sub.i threshold) is considered to be a reference voxel that
includes a respective 3D-data point that is internal to (e.g., part
of but not on any surface of) the presenter 102; and [0236] (iii)
each of the remaining reference voxels i (e.g., those with a
TSDF.sub.i that is between the above-mentioned positive and
negative TSDF thresholds) is considered to be what is referred to
herein as a "surface-candidate voxel"--which is also referred to by
those of skill in the art as an "active voxel," defined herein as a
reference voxel that includes a 3D-data point that is on or at
least sufficiently near a surface of the presenter 102.
[0237] In at least one embodiment, PSS 202 then continues the TSDF
processing by identifying instances of adjoining surface-candidate
reference voxels for which it is the case that (i) one of the
adjoining surface-candidate reference voxels has a positive TSDF
value and (ii) the other of the adjoining surface-candidate
reference voxels has a negative TSDF value. In other words, PSS 202
looks to identify transitions from positive TSDF values to negative
TSDF values, the so-called "zero crossings."
[0238] PSS 202 then "cuts" the 3D-point cloud along the best
approximation of those transition points that the TSDF processing
has identified, and in so doing marks a subset of 3D-data points
from those contained in the identified set of surface-candidate
reference voxels to be considered vertices of the 3D mesh that is
being generated. In carrying out this function, in at least one
embodiment, for each such pair of adjoining reference voxels, PSS
202 selects (as a vertex of the mesh) either the 3D-data point from
the surface-candidate reference voxel that has the positive TSDF
value or the 3D-data point from the surface-candidate reference
voxel that has the negative TSDF value. In at least one embodiment,
PSS 202 selects the 3D-data point from whichever of those two
surface-candidate reference voxels has an associated TSDF value
that is closer to zero (e.g., that has a lower absolute value). In
at least one embodiment, one or more additional iterations of the
above-described TSDF processing are carried out using progressively
smaller reference-voxel volumes, thereby increasing the precision
and accuracy of the TSDF-processing result.
[0239] At this point in the carrying out of step 1604, then, PSS
202 has identified a set of points in the shared geometry 1040 that
PSS 202 has determined to be vertices of the 3D mesh that PSS 202
is generating of the presenter 102. The usefulness of the reference
voxels, reference-voxel centroids, and the like has now been
exhausted in this particular carrying out of step 1604, and such
constructs are not needed and therefore not used until the next
time PSS 202 carries out step 1604, which will, however, be quite
soon (albeit during the next frame).
[0240] iii. Identification of Connected Vertices
(Triangularization)
[0241] After having used TSDF processing to identify the vertices,
PSS 202 in at least one embodiment then identifies pairs of
vertices that are neighboring points on a common surface of the
presenter 102, and stores data that associates these points with
one another, essentially storing data that "draws" of a virtual
line connecting such vertices with one another. To identify
connected vertices, PSS 202 may use an algorithm such as "marching
cubes" (as is known to those of skill in the art) or another
suitable approach.
[0242] By virtue of basic geometry, many groups of three of these
lines will form triangles--e.g., the stored data will reflect that
they form triangles--that together approximate the surface of the
presenter 102. As such, carrying out the marching-cubes (or an
alternative connected-vertices-identifying) algorithm is referred
to herein at times as "triangularizing" the vertices. The
smoothness of that approximation depends in large part on the
density of triangles in the data model as a whole, though this
density can vary from portion to portion of a given 3D mesh of a
given subject such as the presenter 102, perhaps using a higher
triangle density in areas such as the face and hands of the
presenter 102 than is used for areas such as the torso of the
presenter 102, as but one example. In any event, then, a 3D mesh of
a subject such as the presenter 102 can be modeled as a collection
of these triangles, where each such triangle is defined by a unique
set of three mesh vertices.
[0243] In at least one embodiment, each vertex is represented by a
vertex data object--named "meshVertex" by way of example in this
written description--that includes the location of that particular
vertex in the shared geometry 1040. In some embodiments, a vertex
data object also includes connection information to one or more
other vertices. In some embodiments, connected-vertices information
is maintained external to the vertex data objects, perhaps in a
"meshTriangle" data object that includes three meshVertex objects,
or perhaps in a minimum-four-column array where each row
corresponds to a triangle and includes a triangle identifier and
three meshVertex objects. And certainly innumerable other possible
example data architectures could be listed here.
[0244] If a given mesh comprehensively reflects all (or at least
substantially all) of the surfaces of a given subject from every
(or at least substantially every) angle, such that a true
360.degree. experience could be provided, such a mesh is referred
to in the art and herein as a "manifold" mesh. Any mesh that does
not meet this standard of comprehensiveness is known as a
"non-manifold" mesh.
[0245] Whether manifold or non-manifold, a 3D mesh of a subject in
at least one embodiment is a collection of data (e.g., a data
model) that (i) includes (e.g., includes data indicative of,
defining, conveying, containing, and/or the like) a list of
vertices and (ii) indicates which vertices are connected to which
other vertices; in other words, a 3D mesh of a subject in at least
one embodiment is essentially data that defines a 3D surface at
least in part by defining a set of triangles in 3D space by virtue
of defining a set of mesh vertices and the interconnections among
those mesh vertices. And certainly other manners of organizing data
defining a 3D surface could be used as well or instead.
[0246] iv. Mesh Tuning
[0247] A. Introduction
[0248] The above description of 3D-mesh generation (e.g., step 1604
of the method 1600) is essentially a frame-independent, standalone
method for generating a brand-new, fresh mesh for every frame. In
some embodiments, that is what happens--e.g., step 1604 is complete
for that frame. In other embodiments, however, the 3D mesh that
step 1604 generates is not quite ready yet, and one or more of what
are referred to in this disclosure as mesh-tuning processes are
carried out, and it is the result of the one or more mesh-tuning
processes that are carried out in a given embodiment that is the 3D
mesh that is generated in step 1604.
[0249] Such embodiments, including those in which one or more
mesh-tuning processes are carried out prior to step 1604 being
considered complete for a given frame, are referred to herein at
times as "mesh-tuning embodiments." Moreover, in various different
mesh-tuning embodiments, various combinations of mesh-tuning
processes are permuted into various different orders.
[0250] B. Mesh Modification Using a Reference Mesh
[0251] In one or more mesh-tuning embodiments, at least part of a
current mesh is compared to a pre-stored reference mesh models that
reflect standard shape meshes, such as facial models, hand models,
etc. Such reference models may also include pre-identified
features, or feature vertices, such as finger joints, palms, and
other geometries for a hand model, and lip shape, eye shape, nose
shapes, etc., for a face model. One or more modifications of at
least part of the current mesh in light of that comparison result
in a more accurate, realistic representation of a user or chosen
facial features.
[0252] More specifically, in accordance with an embodiment, cameras
with a lower level of detail can be used for full body 3D mesh
generation by creating a hybrid mesh that uses a model to replace
portions of the full body 3D mesh through using specific feature
measurements, such as face feature measurements (or hand feature
measurements) and comparing the measured feature vertices to the
reference feature vertices, and then combining the reference model
with the existing data mesh to generate a more accurate
representation. Thus, low-detail depth cameras, with lower
resolution are capable of being used to generate higher resolution
details of facial features when combined with
statistically-generated models based on known measurements.
[0253] For example, in one embodiment, rather than relying on
specific facial measurements of a specific user obtained from a
depth camera (DC), a pre-existing approximation model is altered
using a video image of the specific user. Image analysis may be
performed to identify a user's facial characteristics such as eye
shape, spacing, nose shape and width, width of face, ear location
and size, etc. These measurements from the video image may be used
to adjust the reference model to make it more closely match the
specific user. In some embodiments, an initial model calibration
procedure may be performed by instructing the user to face directly
at a video camera to enable the system to capture a front view of
the user. The system may also capture a profile view to capture
additional geometric measurements of the user's face (e.g., nose
length). This calibrated reference model can be used to replace
portions of a user's mesh generated from a depth camera, such as
the face. Thus, instead of trying to get more detailed facial depth
measurements, a detailed reference model of the face is adapted to
more closely conform to the user's appearance.
[0254] Thus, in one embodiment a set of vertices can be based on a
high-resolution face model, and combined with lower resolution body
mesh vertices, thereby forming a hybrid mesh.
[0255] FIG. 16B is a flowchart of an exemplary method 1611, in
accordance with at least one embodiment for replacing a facial
component of a 3D mesh of a subject with a facial-mesh model. Like
the method 1600 shown in FIG. 16A, the method 1611 could be carried
out by any number of different entities--or a combination of
multiple entities or components. Thus, VDCs 106, PSS 202, PSS 502
and processors, memories and computer system components illustrated
in FIG. 6 could carry out the method 1611, as will be appreciated
by those of skill in the art.
[0256] Furthermore, the below description of the method 1611 is
given with respect to other elements that are also in the drawings,
though this again is for clarity of presentation and by way of
example, and in no way implies limitation. Each step 1612-1622 is
described in a way that refers by way of example to various
elements in the drawings of the present disclosure.
[0257] Referring now to FIG. 16B in combination with FIG. 16C, step
1612 provides for obtaining a 3D mesh of a subject. For example,
the obtained 3D mesh can be generated from depth-camera-captured
information about the subject. In one embodiment the obtaining the
3D mesh of the subject includes generating the 3D mesh of the
subject from depth-camera-captured information about the subject
via one or more camera assemblies arranged to collect
visible-light-image and depth-image data. As shown in FIG. 2, PSS
202 is shown coupled to set of example VDCs 206, which are capable
of collecting data to enable generating a 3D mesh. Also, in FIG.
16D, several modules are shown that are capable of performing one
or more of the steps shown in FIG. 16B, including
geometric-calculation module 1642, which can calculate a 3D mesh
from received data.
[0258] Step 1614 provides for obtaining a facial-mesh model. In one
embodiment, the facial-mesh model can be obtained via facial-mesh
model storage 1630 shown in FIG. 16C and in other embodiments,
facial-mesh model can be retrieved from data storage 606 shown in
FIG. 16D as facial-mesh model storage 1640. As one of skill in the
art will appreciate, facial-mesh models can also be transmitted to
communication interface 202 and provided as needed.
[0259] Step 1616 provides for locating a facial portion of the
obtained 3D mesh of the subject. For example, as described above, a
full body mesh of a presenter is created and identified portions of
the full body mesh include a facial portion. Thus, PSS 202 included
video-encoding module 404 and geometric-calculation module 402, can
be equipped to identify portions of a full body mesh as facial or
otherwise. Geometric-calculation module 1642 can also be equipped
to identify portions of the full body mesh as will be appreciated
can be located elsewhere within the system described.
[0260] Step 1618 provides for computing a geometric transform based
on the facial portion and the facial-mesh model. In one embodiment,
geometric transform module 1646 shown in FIG. 16D computes the
geometric transform. The geometric transform can include one or
more aggregated error differences between a plurality of feature
points on the facial-mesh model and a plurality of corresponding
feature points on the facial portion of the obtained 3D mesh. In
one embodiment, the geometric transform is based on an affine
transform, such as a rigid transform. More particularly, the
geometric transform can rotate and translate a set of model feature
points so that they align with located feature points (referred to
as landmarks or landmark points) within the system mesh. The
landmark data points can therefore be coarse noisy data as compared
to the higher-resolution facial mesh data from the facial-mesh
model. The landmarks can include one or more locations of facial
features common to both the facial-mesh model and the facial
portion. For example, facial features could include corners of
eyes, locations related to a nose, lips and ears. As will be
appreciated by one of skill in the art, areas of the face with
corners or edge detail may be more likely to enable correspondence
between model and facial portion.
[0261] In one embodiment, the computing the geometric transform can
include identifying the feature points on the facial-mesh model and
the corresponding feature points on the facial portion of the
obtained 3D mesh by locating at least 6 feature points, or between
6 and 845 feature points. In one embodiment, a facial-mesh model
can include up to 3000 feature points. In some embodiments, this
may be characterized as an overdetermined set of equations (e.g.,
25 or 50, or more, using points around the eyes, mouth, jawline) to
determine a set of six unknowns (three rotation angles and three
translations).
[0262] The geometric transform enables a best fit mapping for
translation/scaling and rotation. One exemplary best-fit mapping
could include a minimum-mean squared error (MMSE) type mapping.
Well-known techniques of solving such a system of equations may be
used, such as minimum mean-squared error metrics, and the like.
Such solutions may be based on reducing or minimizing a set of
errors, or an aggregate error metric, based on how closely the
transformed model feature points align to the landmark points.
[0263] Step 1620 provides for generating a transformed facial-mesh
model using the geometric transform. For example, PSS 202 as shown
in FIG. 16C can generate the transformed facial-mesh model. Hybrid
module 1648 shown in FIG. 16D, in one embodiment can be part of PSS
202 and generate the transformed facial-mesh model in combination
with geometric transform module 1646.
[0264] Step 1622 provides for generating a hybrid mesh of the
subject at least in part by combining the transformed facial-mesh
model and at least a portion of the obtained 3D mesh. For example,
in one embodiment, the obtained 3D mesh, minus the facial portion
of the mesh is combined with the facial-mesh model to produce a
hybrid mesh of both facial model and obtained 3D mesh. Thus,
vertices in the original facial portion data mesh are replaced with
the transformed face model.
[0265] In one embodiment generating the transformed facial-mesh
model and generating the hybrid mesh is repeated periodically to
remove accumulated error that could generate over time. Thus,
rather than a frame-by-frame synchronization, the facial model is
synchronized only periodically.
[0266] The final hybrid mesh can then be output via communication
interface 602, or output to peripheral interface 614 as shown in
FIG. 16D. In one embodiment, the hybrid mesh is sent for further
processing to rendering device 112, shown in FIG. 2. Thus, in one
embodiment the hybrid mesh can be a set of geometric-data streams
and/or video streams that are time-synchronized streams sent to a
receiver, such as rendering device 112 or other device.
[0267] One embodiment shown in FIGS. 16B, 16C and 16D in
combination with other FIGs. herein described relates to systems
for generating a hybrid mesh. More specifically, one embodiment
shown in FIG. 16D relates to a system including a memory, such as
data storage 606 including a data storage of one or more
facial-mesh models 1640, each of the one or more facial-mesh models
including high resolution geometric facial image data. The system
further includes a processor 604 coupled to the memory, the
processor 604 including a geometric-calculation module 1642. In one
embodiment, geometric-calculation module 1642 includes a 3D mesh
rendering module 1644 to receive data from one or more one or more
camera assemblies arranged to collect visible-light-image and
depth-image data and create a 3D mesh of a subject, the 3D mesh
including a facial portion. The geometric-calculation module 1642
can further include a geometric transform module 1646 coupled to
the 3D mesh rendering module 1644, the geometric transform module
computing a geometric transform based on the facial portion and one
of the facial-mesh models. In one embodiment, the geometric
transform is determined in response to one or more aggregated error
differences between a plurality of feature points on the
facial-mesh model and a plurality of corresponding feature points
on the facial portion, and the transform is then used to generate a
transformed facial mesh model. The geometric-calculation module
1642 can further include a hybrid module 1648 coupled to the
geometric transform module 1646, the hybrid module generating a
hybrid mesh of the subject at least in part by combining the
transformed facial-mesh model and at least a portion of the
obtained 3D mesh. In one embodiment, the system can further include
a transceiver, which can be communication interface 602 or other
hardware capable of transmitting the hybrid mesh/facial model or
the like as a set of geometric-data streams and video streams as
time-synchronized data streams to a receiver.
[0268] In an alternate embodiment, a system includes at least one
computer and a non-transitory computer readable medium having
stored thereon one or more programs, which when executed by the at
least one computer, cause the at least one computer to obtain a
three-dimensional (3D) mesh of a subject, wherein the obtained 3D
mesh is generated from depth-camera-captured information about the
subject; obtain a facial-mesh model; locate a facial portion of the
obtained 3D mesh of the subject; compute a geometric transform
based on the facial portion and the facial-mesh model, the
geometric transform determined in response to one or more
aggregated error differences between a plurality of feature points
on the facial-mesh model and a plurality of corresponding feature
points on the facial portion of the obtained 3D mesh; generate a
transformed facial-mesh model using the geometric transform;
generate a hybrid mesh of the subject at least in part by combining
the transformed facial-mesh model and at least a portion of the
obtained 3D mesh; and output the hybrid mesh of the subject.
[0269] In one embodiment, once the hybrid mesh is created, a
non-rigid deformation algorithm applies to determine deformation of
the data driven system model. That is, the hybrid mesh can be moved
as close as possible to current-frame depth-image data by using a
non-rigid deformation, explained more fully below with respect to
weighted deformations, below.
[0270] C. Weighted Deformation
[0271] One mesh-tuning process is referred to herein as "weighted
deformation." In short, and stated generally, embodiments that
involve fine-tuning a mesh using a weighted-deformation technique
as described herein involve generating a current mesh in perhaps
the manner described above, and then combining that current mesh
with a "historical" mesh according to a weighting scheme. For
example, then, the 3D mesh that step 1604 ultimately produces could
be the result of a weighted-deformation technique that gives 90%
weight to the historical mesh and 10% weight to the current mesh,
where the historical mesh could be the mesh ultimately generated
from the previous frame, since that mesh itself would also be a
product of hysteresis-type historical weighting, a mathematical
tool that is known in the engineering arts in general.
[0272] In at least one weighted-deformation mesh-tuning embodiment,
PSS 202 does not simply compute a weighted average between the
historical mesh and the current mesh, but instead carries out a
process of actually deforming the historical mesh based at least in
part on the current mesh. Thus, in some such embodiments, the
historical mesh is considered to be a valid position for the
presenter 102, and in the current frame that historical mesh is
allowed to be deformed to better match the current mesh, but only
in restricted ways that are programmed in advance as being valid
amounts and/or types of human motion. Such motion restrictions in
general tend to smooth out and reduce the amount of perceived
jerkiness of motion of the 3D presenter persona 116.
[0273] One way to visualize this mesh deformation is that PSS 202
is deforming the historical (e.g., previous-frame) mesh to look
more similar to the currently generated mesh (than the historical
mesh looks prior to any such deformation). In deforming the
historical mesh, the established connections among vertices (e.g.,
the triangles) stay connected as they are in modeling the surface
of the subject in the historical mesh--they simply get "pulled
along" in various ways that are determined by the current mesh in a
process that is referred to in the art as "non-rigid
deformation."
[0274] There is a process that is known in the art as "optical
flow" that is a 2D analog to the 3D non-rigid deformation of the
historical mesh based on the current mesh that is carried out in at
least one embodiment of the present systems and methods. An example
of an optical-flow algorithm is explained in Michael W. Tao, Jiamin
Bai, Pushmeet Kohli, and Sylvain Paris.: "SimpleFlow: A
Non-Iterative, Sublinear Optical Flow Algorithm". Computer Graphics
Forum (Eurographics 2012), 31(2), May 2012, which is hereby
incorporated herein by reference.
[0275] In some optical-flow implementations and in the
mesh-deformation processes of some embodiments of the present
methods and systems, historical data (such as the historical mesh)
is moved as close as possible to the current data (such as the 3D
mesh generated from current-frame depth-image data), and then an
average (perhaps a weighted average) of the current data and the
post-move historical data is computed. The result of this average
is in some embodiments the 3D mesh that is generated by carrying
out step 1604 of the method 1600. And certainly other
implementations could be used as well.
[0276] As to how mathematically to model the distortion of a given
historical mesh to more closely match a current mesh: in at least
one embodiment, a substantial calculation known in the art as an
energy-minimization problem is carried out. In at least one
embodiment, this energy-minimization problem is carried out with
respect to a subset of the vertices that are referred to herein as
"nodes." In an embodiment, a meshVertex object has a Boolean value
called something akin to "isNode," which is set to "True" if that
meshVertex is a node and is otherwise set to "False." Clearly there
is no end to the variety of ways in which such a toggleable
mesh-vertex property could be implemented.
[0277] In an embodiment, the nodes of the historical mesh (the
"historical-mesh nodes") are compared with the nodes of the current
mesh (the "current-mesh nodes") to determine the extent to which
the presenter 102 moved between the prior frame and the current
frame. On one extreme, if the presenter 102 has not moved at all,
the historical-mesh nodes would match the locations of the
current-mesh nodes on a node-wise basis; in such a situation, the
"energy" would be determined to be zero, and thus not minimizable
any further; the minimization calculation would be complete, the
historical-mesh nodes wouldn't need to be moved at all, and the
historical mesh--or equivalently the current mesh--would become the
step-1604-generated mesh for that frame, perhaps subject to one or
more additional mesh-tuning processes.
[0278] If, however, there is some mismatch between the 3D locations
(in the shared geometry 1040) of the historical-mesh nodes and the
current-mesh nodes, the initial measured energy for that iteration
of the energy-minimization problem would be non-zero (and more
specifically, positive). The historical-mesh nodes would then be
moved (within movement constraints such as those mentioned above)
to more closely align with the current-mesh nodes. When any
historical-mesh node is moved, the connectivity among the triangles
and vertices of the historical mesh is maintained, such that the
connected triangles, vertices, and as a general matter the mesh
surface gets pulled along with the moved historical-mesh node.
[0279] Once all of the historical-mesh nodes have been moved as
much as possible within the allowed constraints to more closely
align with the current-mesh nodes, the energy has been minimized to
the extent possible for that iteration, and the now-modified
historical mesh becomes the step-1604-generated mesh for that
frame, perhaps subject to one or more additional mesh-tuning
processes. There is no reason in principle that every vertex
couldn't be a node, though in most contexts the time and processing
demands would make such an implementation intractable.
[0280] 4. Identification of Respective Lists of Mesh Vertices that
are Visible from the Vantage Point of Each Respective Camera
Assembly
[0281] After PSS 202 has carried out step 1604 for a given
shared-frame-rate time period (e.g., for a given frame), in at
least one embodiment PSS 202 next, at step 1606, calculates three
(and more generally, M) visible-vertices lists, one for each of the
camera assemblies 1024 from which PSS 202 is receiving a raw video
stream 208. And viewed on a broader temporal scale, step 1606 can
be characterized as PSS 202 calculating sets of M visible-vertices
lists at the shared frame rate, where each such visible-vertices
list is the respective subset of the vertices of the current mesh
that is visible in the predetermined coordinate system 1040 from
the vantage point of a respective different one of the M video
cameras of the M camera assemblies.
[0282] In connection with step 1604 in FIG. 16A above, the phrase
"current mesh" refers to the standalone mesh generated for a frame
x based only on information that is current to that frame x without
reference to any historical data. The mesh that results from the
step 1604 is a generated mesh. For purposes of explaining step 1606
however, the current mesh means the mesh that was generated in step
1604 for the current frame. As described above, there are
embodiments in which step 1604 does not involve the use of any
historical data, and there are embodiments in which step 1604 does
involve the use of historical data.
[0283] In connection with step 1606, for each frame, there is a set
of data processing that gets carried out independently from the
vantage point of each of the camera assemblies 1024. For simplicity
of explanation, this set of data processing is explained by way of
example below in connection with the vantage point 1082 of the
camera assembly 1024C, though the reader should understand that
this same processing could also carried out with respect to the
vantage point 1080 of the camera assembly 1024L, and with respect
to the vantage point 1084 of the camera assembly 1024R. This is
true in connection with step 1604 as well, as the processing
described in connection with that step for identifying vertices of
the mesh is conducted from the vantage points of each of the camera
assemblies 1024 as well, though in the case of step 1604, the
processing produces a single data result--the mesh, whereas in the
case of step 1606, the processing produces a respective different
data result from the vantage point of each respective camera
assembly 1024.
[0284] Step 1606 produces a visible-vertices list from the vantage
point of each respective camera assembly 1024. As mentioned above,
the specifics in at least one embodiment of generating a
visible-vertices list is described below in connection with the
vantage point 1082 of the camera assembly 1024C. The term "submesh"
is also used herein interchangeably with "visible-vertices list;" a
contiguous subset of the mesh vertices visible from a given vantage
point can include a submesh of the 3D mesh of the subject (e.g.,
the presenter 102).
[0285] Step 1606--the identification of a visible-vertices list
from a particular vantage point--can be done anywhere on the
communication path between where the data is captured and where the
data is rendered. In some embodiments, such as the method 1600,
this processing is done by PSS 202. In other embodiments, this
processing is done by the rendering device (e.g., HMD 112).
Numerous other possible implementations with respect to which
device or combination of devices carries out the
visible-vertices-list-identification processing, as well as with
respect to where on the above-mentioned communication path this
processing occurs. Identifying a visible vertices list can be more
of a sender-side function, as is the case with the method 1600, and
gives an entity such as PSS 202 the opportunity to compress the
visible-vertices lists prior to transmitting them to the rendering
device. Some example embodiments of mesh compression--including
visible-vertices-list compression (a.k.a. submesh compression)--are
discussed below.
[0286] Identifying a visible-vertices list of a current mesh
(again, the mesh ultimately generated by step 1604 in connection
with the current frame) from the vantage point 1082 of the camera
assembly 1024C, includes identifying which vertices of the current
mesh are visible from the vantage point 1082 of the RGB camera 1102
of the camera assembly 1024C. Identifying can include modeling the
virtual depth camera 1144C as being in exactly the same location in
the shared geometry 1040--and therefore seeing exactly the same
field of view--as the RGB camera 1102 of the camera assembly 1024C,
consistent with the relationship between FIGS. 11B and 11C
(although it is the camera assembly 1024L that is depicted by way
of example there).
[0287] In at least one embodiment, PSS 202 then evaluates the
current mesh from the vantage point of the virtual depth camera
1144C. Using a conceptual framework such as the one displayed and
described in connection with FIGS. 17-19, PSS 202 may go pixel
location by pixel location through a (virtual) 2D pixel array of
the virtual depth camera 1144C. For each such pixel location, PSS
202 may conduct a "Z-delta" analysis to distinguish visible
surfaces (and therefore visible vertices) of the current mesh from
non-visible surfaces (and therefore non-visible vertices) of the
current mesh from the vantage point 1082 of the virtual camera
1144C.
[0288] In conducting this Z-delta analysis for a given pixel
location in the 2D pixel array of the virtual depth camera 1144C,
PSS 202 may carry out operations that simulate drawing a ray that
emanates from the focal point of the virtual depth camera 1144C and
passes through the particular pixel location that is currently
being evaluated. PSS 202 may next determine whether that ray
intersects any of the vertices of the current mesh. If the answer
is zero, no vertex is added to the visible-vertices list for that
pixel location. If there is one, that vertex is added to the
visible-vertices list for that pixel location. If there is more
than one, the vertex with the lowest z-value (e.g., the vertex,
among those intersected by that ray, that is closest to the vantage
point 1082 of the virtual camera 1144C) is added to the
visible-vertices list for that pixel location. As the reader might
suppose, in some embodiments that operate by stepping in positive-z
increments from the vantage point 1082 of the virtual camera 1144C
and frequently evaluating whether a vertex has been intersected,
one is enough and the algorithm can stop searching along that ray.
And certainly other example implementations could be described
here.
[0289] In at least one embodiment, the fact that a given vertex is
visible from a given camera assembly is sufficient to warrant
adding that vertex to the corresponding visible-vertices list. In
other embodiments, however, each visible-vertices list from each
respective vantage point is organized as a list of mesh triangles
for which all three vertices are visible from the given vantage
point. In such embodiments, vertices are only added to the
corresponding visible-vertices lists in groups of three vertices
that (i) form a triangle in the mesh and (ii) are all visible from
the corresponding vantage point. A visible-vertices list that is
organized by mesh triangles is referred to in this disclosure as a
"visible-triangles list," and it should be understood that a
visible-triangles list is a type of visible-vertices list. And
certainly other example implementations could be listed here.
[0290] Whenever a given vertex is added to the visible-vertices
list for the camera assembly 1024C (or any other camera assembly,
though that is the one being used by way of example in this part of
this written description) using an approach such as that described
just above, PSS 202 knows which pixel location in the 2D pixel
array of the virtual depth camera 1144C projects on to that
particular vertex that is being added at that time, and therefore
also knows which pixel location in the corresponding simultaneous
video frame captured by the camera assembly 1024C projects on to
that particular vertex (in embodiments in which the pixel locations
of the virtual 2D pixel array of the virtual depth camera 1144C
correspond on a one-to-one basis with the pixel locations of the
actual 2D pixel array of the RGB camera 1102 of the camera assembly
1024C; if for some reason such an alignment is not present, a
suitable conversion transform can be used to figure out which pixel
location in the 2D pixel array of the RGB camera 1102 corresponds
to a given pixel location in the virtual 2D pixel array of the
virtual depth camera 1144C).
[0291] Thus, since PSS 202 knows which pixel location (the {a,b}
coordinates in the 2D pixel array) corresponds to a given visible
vertex, PSS 202 could convey this information to HMD 112 in the
geometric-data stream 220LCR (or in another data stream), and in at
least one embodiment PSS 202 does just that. PSS 202 need not,
however, and in at least one embodiment does not convey this
information to HMD 112 in the geometric-data stream 220LCR (or in
any other data stream); in at least one embodiment, even though PSS
202 knows which pixel location maps on to a given vertex, PSS 202
elects to save bandwidth by not conveying this information to the
rendering device, and instead leaves it to the rendering device to
"reinvent the wheel" to some extent by figuring out for itself
which pixel location maps to a given vertex in the mesh from a
given vantage point.
[0292] The same is clearly true with the color information of the
corresponding pixel location in the corresponding video frame. PSS
202 could determine that and send it along as well, but information
identification, acquisition, manipulation, and transmission are not
free, and in various different embodiments, explicit and purposeful
choices are made to not send data even though such data is known or
readily knowable by PSS 202, to incur savings in metrics such as
required bandwidth and processing time and burden on the sender
side.
[0293] In at least one embodiment, purposeful and insightful
engineering choices are made to keep what is generally referred to
at times herein as "the color information" (e.g., the video frames
captured by the RGB cameras 1102) separate from and not integrated
with what is generally referred to at times herein as "the
geometric information" (e.g., information such as depth images,
vertices, visible vertices from different perspectives,
interconnections among vertices, and the like) on the sender side
(e.g., at PSS 202) or in transmission between PSS 202 and HMD 112
(see, e.g., the separateness in FIG. 2 of the data streams 218L,
218C, and 218R representing the color information from the
geometric-data stream 220LCR representing the geometric
information).
[0294] And in some embodiments, the separateness of the data into
streams--that are not integrated until they arrive at HMD
112--applies within the category of the color information as well.
Again, reference is made to FIG. 2 where the respective encoded
video streams 218L, 218C, and 218R respectively encode raw video
from the raw video streams 208L, 208C, and 208R. It is known in the
art how to cheaply and efficiently encode a single raw video stream
into a single encoded video stream for transmission across a data
connection to a rendering device; various embodiments represent the
insight that leveraging this knowledge is advantageous to the
overall task of accomplishing virtual teleportation in ways that
provide good user experiences.
[0295] The transmission of the video data in this manner delivers a
full, rich set of color information to the receiver. As described
below, the rendering device uses this color information in
combination with the geometric information to render the
viewpoint-adaptive 3D presenter persona 116. As part of that
viewing experience, a viewer may frequently change their point of
view with respect to the 3D persona 116; and not only that, but in
cases in which the full color information and the accompanying
geometric information is transmitted to multiple different
endpoints, the viewers at those different endpoints will almost
certainly view the 3D persona from different perspectives at least
some of the time. By not pre-blending the color information on the
sender side, each respective viewer can select their own viewpoint
and each get a full-color experience, blended at the receiver side
to account for various vertices being visible from more than one
relevant camera assembly. Thus, in connection with some embodiments
of the present methods and systems, all of the users receive all of
the color information and experience full and rich detail from
their own particular selected perspective.
[0296] 5. Generation of Encoded Video Streams and Geometric-Data
Stream(s)
[0297] a. Introduction
[0298] In at least one embodiment, once PSS 202 has completed the
above-described pixel-location-by-pixel-location identification of
a visible-vertices list (perhaps a visible-triangles list, as the
case may be) from the perspective of each of the camera assemblies
1024L, 1024C, and 1024R, which may be done serially or in parallel
in various different embodiments, as deemed suitable by those of
skill in the art for a given implementation, step 1606 is complete,
and PSS 202 proceeds, at step 1608, to generating at least M+1 (or
at least 4 in the described example embodiment) separate
time-synchronized data streams at the shared frame rate. The at
least M+1 (in this case, 4) separate time-synchronized data streams
include (i) M (in this case, 3) encoded video streams 218 that each
encode a respective different one of the received (raw) video
streams 208 and (ii) a set of one or more geometric-data streams
220LCR *** that collectively conveys the visible-vertices lists
that were generated in step 1606.
[0299] b. The Color Information
[0300] i. Generally
[0301] It is described in other parts of this disclosure, that each
of the encoded video streams 218 encodes a respective different one
of the received video streams 208. In at least one embodiment, the
encoded video streams 218 do not contain any data that is referred
to herein as geometric information. In at least one embodiment, the
geometric-data stream 220LCR does not contain any data that is
referred to herein as color information. In at least one
embodiment, (a) the encoded video streams 218 do not contain any
data that is referred to herein as geometric information and (b)
the geometric-data stream 220LCR does not contain any data that is
referred to herein as color information.
[0302] ii. Background Removal
[0303] In at least one embodiment, the encoded video streams 218
convey full (e.g., rectangular) frames of color information. The
encoded video streams 218 may or may not include standalone
i-frames as they are known in the art. In some embodiments, that is
the case; in other embodiments, the encoded video streams 218 make
use of inter-frame-referential constructs such as p-frames to
reduce the amount of bandwidth occupied by the encoded video
streams 218.
[0304] In other embodiments, however, the encoded video streams 218
do not convey full (e.g., rectangular) frames of detailed color
information. Instead, in some embodiments, the encoded video
streams convey frames that only have detailed color information for
pixels that represent the subject (e.g., the presenter 102), and in
which the rest of the pixels in the (still-rectangular-shaped)
frames are filled in with a particular color known as a chromakey,
selected in some embodiments to be a color that does not occur or
at least rarely occurs in the image of the presenter 102
itself.
[0305] The fact that a given frame includes detailed color
information of the subject and is chromakeyed everywhere else does
not convert such a video frame into being one that conveys or
contains geometric information. Even though the subject has been
isolated and surrounded by a chromakey in the video frames, those
video frames still include no indication of which color pixels
project on to which vertices; the color frames know nothing of
vertices. In that sense, chromakey embodiments are not all that
different from non-chromakey embodiments, other than being lighter
on required bandwidth, since both types of embodiments ultimately
turn to the geometric information to identify color pixels that map
onto mesh vertices: the chromakey embodiments simply involve
transmission ultimately of fewer detailed color pixels.
[0306] In at least one embodiment, the removal of background pixels
(or the extraction of pixels that represent the subject, or "user
extraction") is performed using "alpha masks" which identify the
pixel locations belonging to a desired persona (e.g., user). A
given alpha mask may take the form of or at least include an array
with a respective stored data element corresponding to each pixel
in the corresponding frame, where such stored data elements are
individually and respectively set equal to 1 (one) for each user
pixel and to 0 (zero) for every other pixel (i.e., for each
non-user (a.k.a. background) pixel).
[0307] The described alpha masks correspond in name with the
definition of the "A" in the "RGBA" pixel-data format known to
those of skill in the art, where "R" is a red-color value, "G" is a
green-color value, "B" is a blue-color value, and "A" is an alpha
value ranging from 0 (complete transparency) to 1 (complete
opacity). In a typical implementation, the "0" in the previous
sentence may take the form of a hexadecimal number such as 0x00
(equal to a decimal value of 0 (zero)), while the "1" may take the
form of a hexadecimal number such as 0xFF (equal to a decimal value
of 255); that is, a given alpha value may be expressed as an 8-bit
number that can be set equal to any integer that is (i) greater
than or equal to zero and (ii) less than or equal to 255. Moreover,
a typical RGBA implementation provides for such an 8 bit alpha
number for each of what are known as the red channel, the green
channel, and the blue channel; as such, each pixel has (i) a red
("R") color value whose corresponding transparency value can be set
to any integer value between 0x00 and 0xFF, (ii) a green ("G")
color value whose corresponding transparency value can be set to
any integer value between 0x00 and 0xFF, and (iii) a blue ("B")
color value whose corresponding transparency value can be set to
any integer value between 0x00 and 0xFF. And certainly other
pixel-data formats could be used, as deemed suitable by those
having skill in the relevant art for a given implementation.
[0308] When merging an extracted persona with content, the
disclosed methods and/or systems may create a merged display in a
manner consistent with the related applications previously cited;
in particular, on a pixel-by-pixel (i.e., pixel-wise) basis, the
merging is carried out using pixels from the captured video frame
for which the corresponding alpha-mask values equal 1, and
otherwise using pixels from the content.
[0309] c. The Geometric Information
[0310] i. Generally
[0311] As stated above, among the data streams that PSS 202
generates as part of carrying out step 1608 is the geometric-data
stream 220LCR. In at least one embodiment, PSS 202 generates and
sends three separate geometric data streams: geometric-data stream
220L associated with the camera assembly 1024L, geometric-data
stream 220C associated with the camera assembly 1024C, and
geometric-data stream 220R associated with the camera assembly
1024R. In other embodiments, PSS 202 generates and sends a single
geometric-data stream 220LCR that conveys geometric data (e.g.,
visible-vertices lists) associated with all three of the camera
assemblies 1024L, 1024C, and 1024R. This distinction not being
overly important, as mentioned above, whether one, three, or some
other number of geometric-data streams are used, they are
collectively referred to herein as the geometric-data stream 220LCR
or more simply the geometric-data stream 220.
[0312] In at least one embodiment, the geometric-data stream 220
conveys each visible-vertices list as simply a list or array of
meshVertex data objects, where each such meshVertex includes its
coordinates in the shared geometry 1040. In other embodiments, each
meshVertex also includes data identifying one or more other
meshVertexes to which the instant meshVertex is connected. In some
embodiments, each visible-vertices list includes a list of
meshTriangle data objects that each include three meshVertex
objects that are implied by their inclusion in a given meshTriangle
data object to be connected to one another. In other embodiments,
the visible-vertices list takes the form of an at-least-four-column
array where each row includes a triangle identifier and three
meshVertex objects (or perhaps identifiers thereof).
[0313] Clearly there are innumerable ways in which a given
visible-vertices list can be arranged for conveyance from PSS 202
to HMD 112, and the various possibilities offered here are merely
illustrative examples. Some further possibilities are detailed
below in connection with the topic of submesh compression.
[0314] ii. Camera Intrinsics and Extrinsics
[0315] In at least one embodiment, in order to provide HMD 112 (or
other rendering system or device) with sufficient information to
render the 3D presenter persona 116, PSS 202 transmits to HMD 112
what is referred to herein as camera-intrinsic data (or "camera
intrinsics" or simply "intrinsics," a.k.a.
"camera-assembly-capabilities data") as well as what is referred to
herein as camera-extrinsic data (or "camera extrinsics" or simply
"extrinsics," a.k.a. "geometric-arrangement data"). And it is
explicitly noted that, although this topic is addressed in this
disclosure as a subsection of step 1608, the transmission of the
camera-intrinsic data and the camera-extrinsic data could be done
only a single time and need not be done repeatedly (unless some
modification occurs and an update is needed, for example).
[0316] In at least one embodiment, the camera-intrinsic data
includes one or more values that convey inherent (e.g., configured,
manufactured, physical, and in general either permanently or at
least semi-permanently immutable) properties of one or more
components of the camera assemblies. Examples include focal
lengths, principal point, skew parameter, and/or one or more
others. In some cases, both a focal length in the x-direction and a
focal length in the y direction are provided; in other cases, such
as may be the case with a substantially square pixel array, the
x-direction and y-direction focal lengths may be the same and as
such only a single value would be conveyed.
[0317] In at least one embodiment, the camera-extrinsic data
includes one or more values that convey aspects of how the various
camera assemblies are arranged in the particular implementation at
hand. Examples include location and orientation in the shared
geometry 1040.
[0318] iii. Submesh Compression
[0319] A. Introduction
[0320] Bandwidth is often at a premium, and the efficient use of
available bandwidth is an important concern. When a mesh is
generated on the sender side and transmitted to the receiving side,
reducing the amount of data needed to convey the visible-vertices
lists is advantageous. Among the benefits of bandwidth conservation
with respect to the geometric information is that it increases the
relative amount of available bandwidth available to transmit the
color information, and increases the richness of the color
information conveyed in a given implementation.
[0321] The terms "mesh compression," "submesh compression," and
"visible-vertices-list compression" are used relatively
interchangeably herein. Among those terms, the one that is used
most often in this description is submesh compression, and just as
"submesh" is basically synonymous with "visible-vertices list" in
this description, so is "submesh compression" basically synonymous
with "visible-vertices-list compression." The term "mesh
compression" can either be thought of as (i) a synonym of "submesh
compression" (since a submesh is still a mesh) or (ii) as a
collective term that includes (a) carrying out submesh compression
with respect to each of multiple submeshes of a given mesh, thereby
compressing the mesh by compressing its component submeshes and can
include (b) carrying out one or more additional functions (such as
duplicative-vertex reduction, as described) with respect to one or
more component submeshes and/or the mesh as a whole.
[0322] In the ensuing paragraphs, various different measures that
are taken in various different embodiments to effect submesh
compression are described. In each case, unless otherwise noted,
each described submesh-compression measure is described by way of
example with respect to one submesh (though not one particular
submesh) though it may be the case that such a measure in at least
one embodiment is carried out with respect to more than one
submesh.
[0323] B. Reducing Submesh Granularity
[0324] In at least one embodiment, a submesh-compression measure
that is employed with respect to a given submesh is to simplify the
submesh by reducing its granularity--in short, reducing the total
number of triangles in the submesh. Doing so reduces the amount of
geometric detail that the submesh includes, but this is a tradeoff
that may be worth it to free up bandwidth for richer color
information.
[0325] As a general matter, the flatter a given surface is (or is
being modeled to be), the fewer triangles one needs to represent
that surface. It is further noted that another way to express a
reduction in submesh granularity is as a reduction in triangle
density of the submesh--which can be the average number of
triangles used to represent the texture of a given amount of
surface area of the subject.
[0326] In some embodiments, some submesh compression is
accomplished by reducing the triangle density in some but not all
of the regions of a given submesh. For example, in some
embodiments, detail may be retained (e.g., using a higher triangle
density) for representing body parts such as the face, head, and
hands, while detail may be sacrificed (e.g., using a lower triangle
density) for representing body parts such as a torso. In other
embodiments, the triangle density is reduced across the board for
an entire submesh. And certainly other example implementations
could be described here.
[0327] Whether a reduction in triangle density is carried out for
all of a given submesh or only for one or more portions of the
given submesh, there are a number of different algorithms known to
those of skill in the art for reducing the granularity of a given
triangle-based mesh. One such algorithm essentially involves
merging nearby vertices and then removing any resulting zero-area
triangles from the particular submesh.
[0328] To give the reader an idea of the order of magnitude both
before and after a triangle-granularity-reduction operation such as
is being described here, it may be the case that the "before
picture" is a submesh that has about 50,000 triangles among about
25,000 vertices and that the "after picture" is a submesh that has
about 30,000 triangles among about 15,000 vertices. These numbers
are offered purely by way of example and not limitation, as it is
certainly the case that (i) a given "before picture" of a given
submesh could include virtually any number of triangles, though the
number of triangles of course bears some relation to the
corresponding number of vertices from which those triangles are
formed and (ii) various different algorithms for reducing the
granularity of a triangle-based mesh would have different reduction
effects on the triangle density.
[0329] C. Stripifying the Triangles
[0330] 1. Introduction
[0331] In at least one embodiment, a submesh-compression measure
that is employed for a given submesh includes stripification or a
stripifying of the triangles. An example stripification embodiment
is depicted in and described in connection with FIG. 20, which is a
flowchart of a method in accordance with at least one embodiment.
As is described above with respect to method 1600, method 2000
could be carried out by any CCD that is suitably equipped,
configured, and programmed to carry out the functions described
herein in connection with stripification of mesh triangles, submesh
triangles, and the like. By way of example and not limitation, the
method 2000 is described herein as being carried out by PSS
202.
[0332] In some embodiments, method 2000 is a substep of step 1608,
in which PSS 202 generates the geometric-data stream(s) 220LCR. In
short, method 2000 can be thought of as an example way for PSS 202
to transition from having full geometric information about the mesh
that it just generated to having a compressed, abbreviated form of
that geometric information that can be more efficiently transmitted
to a receiving device for reconstruction of the associated mesh and
ultimately rendering of the 3D presenter persona 116.
[0333] FIG. 21 is a first view of an example submesh 2104 of part
of the presenter 102, shown for the shared geometry 1040. Unlike
FIGS. 17-19, the shared geometry 1040 is depicted in FIG. 21 from
the same perspective as is used in, e.g., FIG. 10B. Among the
reasons for using the rotated views in FIGS. 17-19 was to show the
orientation of the shared geometry 1040 for another coordinate
system, which in those figures was a 2D pixel array.
[0334] As depicted in FIG. 21, the submesh 2102 includes a section
2104 depicted in this example as being on the right arm of the
presenter 102. The section 2104 spans x-values from x2106 to x2108
and y-values from y2110 to y2112, all four of which are arbitrary
values. FIGS. 21 and 22 are explained herein without explicit
reference to the z-dimension; all of the vertices discussed are
assumed to have a constant z-value that is referred to here as
z2104 (the arbitrarily selected constant z-value of the section
2104 of the submesh 2102). In a typical operation there would be a
number of different z-values among the various vertices of the
section 2104, to show the contours of that part of the right arm of
the presenter 102. Each of the vertices 2202-2232, therefore, has a
location in the shared geometry 1040 that can be expressed as
follows, using the vertex 2218 as an example: xyz1040::{x2218,
y2218, z2104}.
[0335] FIG. 22 depicts a view 2200 that includes the submesh 2102
and the section 2104, and that also includes a magnified version of
the section 2104. As can be seen in FIG. 22, the section 2104
contains 16 vertices that are numbered using the even numbers
between 2202 and 2232, inclusive. These 16 vertices 2202-2232 are
shown as forming 18 triangles numbered using the even numbers
between 2234 and 2268, inclusive. The triangles 2234-2268 are
organized into two strips. In particular, the triangles 2234-2250
form a strip 2270, and the triangles 2252-2268 form a strip 2272.
This section 2104, these vertices 2202-2232, these triangles
2234-2268, and these strips 2270 and 2272 are used as an example
data set for embodiments of the method 2000.
[0336] At step 2002, PSS 202 obtains the triangle-based 3D mesh (in
this case, the submesh 2102) of a subject (e.g., the presenter
102). In an embodiment, PSS 202 carries out step 2002 at least in
part by carrying out the above-described steps 1604 and 1606, which
results in the generation of three meshes: the submesh from the
perspective of the camera assembly 1024L, the submesh from the
perspective of the camera assembly 1024C, and the submesh from the
perspective of the camera assembly 1024R. In this example, the
submesh 2104 is from the perspective of the camera assembly
1024C.
[0337] At step 2004, PSS 202 generates a triangle-strip data set
that represents a strip of triangles in the submesh 2102. In the
below-described examples, PSS 202 generates a triangle-strip data
set to represent the strip 2270. Finally, at step 2006, PSS 202
transmits the generated triangle-strip data set to a
receiving/rendering device such as the HMD 112 for reconstruction
by the HMD of the submesh 2102 and ultimately for rendering by the
HMD 112 of the viewpoint-adaptive 3D persona 116. Example ways in
which PSS 202 may carry out step 2004 are described below.
[0338] In some embodiments, PSS 202 stores each vertex as a
meshVertex data object that includes at least the 3D coordinates of
the instant vertex in the shared geometry 1040. Furthermore, PSS
202 may store a given triangle as a meshTriangle data object that
itself includes three meshVertex objects. PSS 202 may further store
each strip as a meshStrip data object that itself includes some
number of meshTriangle objects. Thus, in one embodiment, PSS 202
carries out step 2004 by generating a meshStrip data object for the
strip 2270, wherein that meshStrip data object includes a
meshTriangle data objects for each of the triangles 2234-2250, and
wherein each of those meshTriangle data objects includes a
meshVertex data object for each of the three vertices of the
corresponding triangle, wherein each such meshVertex data object
includes a separate 8-bit floating point number for each of the
x-coordinate, the y-coordinate, and the z-coordinate of that
particular vertex.
[0339] This approach would involve PSS 202 conveying the strip 2270
by sending a meshStrip object containing nine meshTriangle objects,
each of which includes three meshVertex objects, each of which
includes three 8-bit floating-point values. That amounts to 81
8-bit floating-point values, which amounts to 648 bits without even
counting any bits for the overhead of the data-object structures
themselves. But using 648 bits as a floor, this approach gets
metrics of using 648 bits to send nine triangles, which amounts to
72 bits per triangle (bpt) at best. In terms of bits per vertex
(bpv), which is equal to 1/3 of the bpt (due to there being three
vertices per triangle); in this case the described approach
achieves 24 bpv at best. Even a simplified table or array
containing all of these vertices could do no better than 72 bpt and
24 bpv.
[0340] An even more brute-force, naive approach would be one in
which each of the 27 transmitted vertices not only includes three
8-bit floats for the xyz coordinates, but also includes color
information in the form of an 8-bit red value, an 8-bit green (G)
value, and an 8-bit blue (B) value. As each vertex would then
require six 8-bit values instead of three 8-bit values, doing this
would double the bandwidth costs to 1296 total bits for the strip
2270 (144 bpt and 48 bpv). These numbers are offered by way of
comparison to various embodiments, not by way of suggestion.
[0341] The triangle 2234 includes the vertices 2222, 2210, and
2220. The triangle 2236 includes the vertices 2210, 2220, and 2208.
The triangle 2236 differs from the triangle 2234, therefore, by
only a single vertex: the vertex 2208 (and not the vertex 2222).
Thus, in at least one embodiment, once all three vertices of a
given triangle have been conveyed to a recipient, with those three
vertices ordered such that, for example, the second and third
listed of those three vertices are implied to be part of the next
triangle, that next triangle can be specified with only a single
vertex.
[0342] In an embodiment, PSS 202 and the HMD 112 both understand
that for a strip of triangles to be conveyed, the first such
triangle will be specified by all three of its vertices listed in a
particular first, second, and third order. The second such triangle
will be specified with only a fourth vertex and the implication
that the triangle also includes the second and third vertices from
the previous triangle. The third such triangle can be specified
with only a fifth vertex and the implication that the third
triangle also includes the third and the fourth vertices that have
been specified, and so on.
[0343] An approach such as this would need nine 8-bit floats (short
for "floating-point values or numbers") to fully specify the
{x,y,z} coordinates of the three vertices 2222, 2210, and 2220 of
the triangle 2234. For each of the second through ninth triangles
2236-2250, however, only a single vertex (e.g., three 8-bit floats)
would need to be specified for each. Therefore, this same strip
2270 of nine triangles could be sent using three 8-bit floats for
each of 11 vertices, for a total of 11 vertices*3 floats/vertex*8
bits/float=264 bits, which amounts to 29.33 bpt and 9.78 bpv.
[0344] 2. Space-Modeling Parameters
[0345] In at least one embodiment, there are two space-modeling
parameters that are relevant to the precision and scale that can be
represented, as well as to the bandwidth that will be required to
do so. These two space-modeling parameters are referred to herein
as the "cube-side size" and the "cube-side quantization."
[0346] The cube-side size is a real-world dimension that
corresponds to each side (e.g., length, width, and depth) of a
single (imaginary or virtual) cube of 3D space that the subject
(e.g., the presenter 102) is considered to be in. In at least one
embodiment, the cube-side size is two meters, though many other
values could be used instead, as deemed suitable by those of skill
in the art. In some embodiments, a cube-side-size of two meters is
used for situations in which a presenter is standing, while a
cube-side size of one meter is used for situations in which a
presenter is sitting (and only the top half of the presenter is
visible). Certainly many other example cube-side sizes could be
used in various different embodiments, as deemed suitable by those
of skill in the art for a given implementation.
[0347] The cube-side quantization is the number of bits available
for subdivision of the cube-side size (e.g., the length of each
side of the cube) into sub-segments. If the cube-side quantization
were one, each side of the cube could be divided and resolved into
only two parts (0, 1). If the cube-side quantization were two, each
side of the cube could be divided into quarters (00, 01, 10, 11).
In at least one embodiment, the cube-side quantization is 10,
allowing subdivision (e.g., resolution) of each side of the cube
into 210 (e.g., 1024) different sub-segments, though many other
values could be used instead, as deemed suitable by those of skill
in the art. The cube-side quantization, then, is a measure of how
many different pixel locations will be available (to hold
potentially different values from one another) in each of the
x-direction, the y-direction, and the z-direction in the shared
geometry 1040.
[0348] In an embodiment in which the cube-side size is two meters
and the cube-side quantization is 10, the available two meters in
the x-direction, the available two meters in the y-direction, and
the available two meters in the z-direction are each resolvable
into 1024 different parts that each have a length in their
respective direction of two meters/side*side/1024 sub-segments*1000
mm/m=.sup..about.1.95 millimeters (mm). This result (1.95 mm in
this case) is referred to herein as the "step size" of a given
configuration, and it will be understood by the reader having the
benefit of this disclosure that the step size is a function of both
the cube-side size and the cube-side quantization, and that
changing one or both of those space-modeling parameters would
change the step size (unless of course, they were both changed in a
way that produced the same result, such as a cube-side size of one
meter and a cube-side quantization of nine (such as one meter
divided into 29 (512) steps and then multiplied by 1000 mm/m also
yields a step size of 1.95 mm)). Using an example cube-side size of
two meters and an example cube-side quantization of 10, then, the
atomic part of the mesh is a cube that is .sup..about.1.95 mm along
each side. In some instances, 3D pixels are known as voxels.
[0349] In this disclosure, the "step size" is the smallest amount
of distance that can be moved (e.g., "stepped") in any one
direction (e.g., x, y, or z), somewhat analogous to what is known
in physics circles (for our universe) as the "Planck length," named
for renowned German theoretical physicist Max Planck and generally
considered to be on the order of 10.sup.-35 meters (and of course
real-world movement of any distance is not restricted to being
along only one of three permitted axial directions).
[0350] 3. Expressing Vertices in Step Sizes
[0351] Some examples given above of a few different ways in which
PSS 202 could carry out step 2004 using an 8-bit float to express
every x-coordinate, y-coordinate, and z-coordinate of every vertex.
Given the above discussion regarding the cube-side size, the
cube-side quantization, and the step size, some parallel examples
are given in this sub-section where a 10-bit number of steps is
used rather than an 8-bit float to express any absolute
x-coordinate, y-coordinate, or z-coordinate values.
[0352] Revisiting the example in which PSS 202 transmitted all 81
coordinates of the 27 vertices of the 9 triangles in the strip
2270, mapping that brute-force, naive approach on to use of step
sizes, that approach would require the transmission of 81
coordinates*10 bits/coordinate=810 bits total (90 bpt and 30 bpv).
Not surprising that using two extra bits per coordinate raised the
overall bandwidth cost.
[0353] Now revisiting the example in which PSS 202 needed 264 bits
to send an 8-bit float for each coordinate of each of the 11
vertices in the strip 2270, using 10-bit step counts (from the
origin (e.g., {0,0,0}) of the shared geometry 1040) instead of
8-bit floats would again raise the bandwidth cost, this time to 11
vertices*3 step counts/vertex*10 bits/step count=330 total bits
(36.67 bpt and 12.22 bpv).
[0354] 4. Replacing Coordinate Values with Coordinate Deltas
[0355] Some embodiments involve expression of a coordinate (e.g.,
an x-coordinate) using not an absolute number (a floating-point
distance or an integer number of steps) from the origin but rather
using a delta for another (e.g., the immediately preceding) value
(e.g., the x-coordinate specified immediately prior to the
x-coordinate that is currently being expressed using an
x-coordinate delta). In some embodiments, assuming that a preceding
vertex was specified in some manner (either with absolute values
from origin or using deltas from its preceding vertex), a current
vertex is denoted delta-x, a delta-y, and a delta-z for that
immediately preceding vertex.
[0356] Step size is relevant in embodiments in which a delta in a
given axial direction is expressed in an integer number of "steps"
of size "step size." Therefore, when it comes to considerations of
bandwidth usage, the number of bits that is allocated for a given
delta determines the maximum number of step sizes for a given
coordinate delta. This adjustable parameter is similar in principle
to the cube-side quantization discussed above, in that a number of
bits naturally determines a number of unique values that can be
represented by such bits (# of values=2.sup.# of bits).
[0357] The number of bits allocated in a given embodiment to
express a delta in a given axial direction (a delta-x, a delta-y,
or a delta-z) is referred to as the "delta allowance" (and is
referred for the particular axial directions as the "delta-x
allowance," the "delta-y allowance," and the "delta-z allowance").
A related value is the "max delta," which in this disclosure refers
to the maximum number of step sizes in any given axial direction
that can be specified by a given delta. If a delta allowance is
two, the max delta is three (e.g., "00" could specify zero steps
(e.g., the same x-value as the previous x-value), "01" could
specify one step, "10" could specify two steps, and "11" could
specify three steps). In at least one embodiment, the delta
allowance is four and the max delta is therefore 15, though
certainly many other numbers could be used instead.
[0358] Those examples assume that the progression in a given
dimension would always be positive (e.g., a delta-x of three would
mean "go three steps the (implied positive) x-direction"). This may
not be the case, however, and therefore in some embodiments a delta
allowance of, e.g. four, would still permit expression of 16
different values, but in a given implementation, perhaps seven of
those would be negative (e.g. "one step in the negative direction"
through "seven steps in the negative direction"), one would be "no
steps in this axial direction", and the other eight would be
positive (e.g., "1 step in the positive direction" through "eight
steps in the positive direction"). And certainly numerous other
example implementations could be listed here.
[0359] Returning now to example ways in which PSS 202 could carry
out step 2004, the two examples above in which PSS 202 compressed
the strip 2270 by sending all three vertices for the first
triangle, and then only one vertex for each ensuing triangle, each
time implying that the current triangle is formed from the newly
specified vertex and the two last-specified vertices of the
preceding triangle. Taking this approach using absolute coordinates
expressed in 8-bit floats incurred a bandwidth cost of 264 total
bits (29.33 bpt and 9.78 bpv), and taking this approach using
absolute coordinates in 10-bit step counts incurred a bandwidth
cost of 330 total bits (36.67 bpt and 12.22 bpv).
[0360] In at least one embodiment, PSS 202 uses the following
approach for compressing and transmitting the strip 2270. The first
triangle is sent using three 10-bit step counts from origin for the
first vertex, three 4-bit coordinate deltas from the first vertex
for the second vertex, and three 4-bit coordinate deltas from the
second vertex for the third vertex (for a total of 38 bits so far
(38 bpt and 12.67 bpv)). The second triangle is sent as just the
fourth vertex in the form of three 4-bit coordinate deltas from the
third vertex (for a total of 50 bits so far (25 bpt and 16.67
bpv)). The third triangle is sent as just the fifth vertex in the
form of three 4-bit coordinate deltas from the fourth vertex (for a
total of 62 bits so far (20.67 bpt and 6.89 bpv)). By the time the
ninth (of the nine) triangles is sent--as just the eleventh vertex
in the form of three 4-bit coordinate deltas from the tenth vertex,
the total bandwidth cost for the whole strip 2270 is 134 bit total
(14.89 bpt and 4.96 bpv).
[0361] In some embodiments, as demonstrated in the explanation of
the prior example, the more triangles in a given strip, the better
the bpt and bpv scores become, since each additional triangle only
incurs the cost of a single vertex, whether that single vertex be
expressed as three 8-bit floats, three 10-bit step counts, three
4-bit coordinate deltas, or some other possibility. In the case of
the example described in the preceding paragraph, the bpt would
continue to approach (but never quite reach) 12 and the bpv would
continue to approach (but never quite reach) four, though these
asymptotic limits can be shattered by using other techniques such
as the entropy-encoding techniques described below. Other similar
examples are possible as will be appreciated by one of skill in the
art.
[0362] In at least one embodiment, to minimize the amount of data
that is being moved around during--and the amount of time needed
for--the stripification functions, PSS 202 generates a table of
submesh vertices where each vertex is assigned a simple identifier
and is stored in association with its x, y, and z coordinates,
perhaps as 8-bit floats or as 10-bit step counts. This could be as
simple as a four-column array where each row contains a vertex
identifier for a given vertex, the x-coordinate for that given
vertex, the y-coordinate for that given vertex, and the
z-coordinate for that given vertex. As with a number of the other
aspects of this disclosure, the number of bits allotted for
expressing vertex identifiers puts an upper limit on the number of
vertices that can be stored in such a structure, though such
limitations tend to be more important for transmission operations
than they are for local operations such as vertex-table
management.
[0363] 5. Encoding Entropy
[0364] Some embodiments use entropy-encoding mechanisms to further
reduce the bpt and bpv scores for transmission of strips of
triangles of triangle-based meshes. This is based on the insight
that a great many of the triangles in a typical implementation tend
to be very close to being equilateral triangles, which means that
there are particular values for delta-x, delta-y, and delta-z that
occur significantly more frequently than other values. To
continuously keep repeating that same value in coordinate delta
after coordinate delta would be unnecessarily wasteful of the
available bandwidth. As such, in certain embodiments, PSS 202
encodes frequently occurring coordinate-delta values using fewer
than four bits (or whatever the delta allowance is for the given
implementation). One way that this can be done is by using Huffman
encoding, though those of skill in the art will be aware of other
encoding approaches as well.
[0365] 6. Reducing the Number of Duplicative Receiver-Side
Vertices
[0366] As described above, some embodiments involve the compression
and transmission of triangle strips using coordinate deltas instead
of absolute coordinates to specify particular vertices to the
receiver. Thus, using FIG. 22 for reference, PSS 202 may first
compress the strip 2270 as described above for transmission to the
receiver and then proceed to compressing the strip 2272 in a
similar fashion, also for transmission to the receiver. In
compressing each of these strips 2270 and 2272 for transmission
using coordinate deltas, it won't be long until PSS 202 encodes
some vertices that it has already sent to the receiver.
[0367] In an example sequence, PSS 202, as part of compressing and
transmitting the first strip 2270, transmits the following eleven
vertices in the following order:
[0368] 1. vertex 2222 (30 bits of absolute step-count
coordinates);
[0369] 2. vertex 2210 (12 bits of coordinate deltas);
[0370] 3. vertex 2220 (12 bits of coordinate deltas);
[0371] 4. vertex 2208 (12 bits of coordinate deltas);
[0372] 5. vertex 2218 (12 bits of coordinate deltas);
[0373] 6. vertex 2206 (12 bits of coordinate deltas);
[0374] 7. vertex 2216 (12 bits of coordinate deltas);
[0375] 8. vertex 2204 (12 bits of coordinate deltas);
[0376] 9. vertex 2214 (12 bits of coordinate deltas);
[0377] 10. vertex 2202 (12 bits of coordinate deltas); and
[0378] 11. vertex 2212 (12 bits of coordinate deltas).
[0379] Upon starting the compression of the strip 2272 (and
assuming that, as would tend to be the case from time to time, PSS
202 has to revert to sending a full 30-bit expression of the
step-size coordinates of a given triangle, and then resume the
coordinate-delta approach), PSS 202, as part of compressing and
transmitting the first strip 2270, transmits the following eleven
vertices in the following order, wherein the list numbering is
continued purposefully from the previous numbered list:
[0380] 12. vertex 2222 (30 bits of absolute step-count
coordinates);
[0381] 13. vertex 2232 (12 bits of coordinate deltas);
[0382] 14. vertex 2220 (12 bits of coordinate deltas);
[0383] 15. vertex 2230 (12 bits of coordinate deltas);
[0384] 16. vertex 2218 (12 bits of coordinate deltas);
[0385] 17. vertex 2228 (12 bits of coordinate deltas);
[0386] 18. vertex 2216 (12 bits of coordinate deltas);
[0387] 19. vertex 2226 (12 bits of coordinate deltas);
[0388] 20. vertex 2214 (12 bits of coordinate deltas);
[0389] 21. vertex 2224 (12 bits of coordinate deltas); and
[0390] 22. vertex 2212 (12 bits of coordinate deltas).
[0391] It can be seen, then, that PSS 202 transmitted the following
duplicate vertices: [0392] vertex 2222 was sent as both 1 and 12 on
the list (in full 30-bit form, no less, though that would actually
help the receiver remove the second occurrence as a duplicate
vertex); [0393] vertex 2220 was sent as both 3 and 14 on the list
(in only coordinate-delta form, as is the case with the remaining
items in this list of duplications, thus offering little help to
the receiving device in identifying the duplicative-vertex
transmission); [0394] vertex 2218 was sent as both 5 and 16 on the
list; [0395] vertex 2216 was sent as both 7 and 18 on the list;
[0396] vertex 2214 was sent as both 9 and 20 on the list; and
[0397] vertex 2212 was sent as both 11 and 22 on the list.
[0398] In some instances, the ratio of transmitted vertices to
actual vertices (e.g., unique vertices in the mesh on the sender
side) is close to two. One possible workaround for this issue is to
transmit a unique index for each vertex. However, as discussed
above, even after simplification, there is often on the order of
15,000 unique vertices in the mesh on the server side. As such, it
would require 14 bits per vertex to include such a vertex
identifier (where 14 bits provides for 16,384 different possible
binary identifiers). Thus, it is "cheaper" in the bandwidth sense
to send a 12-bit (such as three 4-bit coordinate deltas) vertex
twice than it would be to send such a vertex identifier with every
unique vertex.
[0399] When receiving compressed-submesh information, the receiver
compiles a list of submesh vertices, and that last include a
significant number of duplicates, often approaching half of the
total number of vertices. This places an undue processing burden on
the receiver in a number of ways. First, the receiver simply has to
add nearly twice as many vertices to its running list of vertices
than it would if there were no duplicates. Second, the receiver is
then tasked with rendering what it believes without any reason not
to is a mesh with, say, 28,000 vertices in it instead of the 15,000
that are in the mesh data model on the sender side (for
representing the same subject in the same level of geometric
detail). This causes problems such as the rendering device
wastefully using spots in its rendering (e.g., vertex) cache.
[0400] The receiver could carry out functions such as sorting and
merging to remove duplicate vertices, but this too is
computationally expensive. Another looming problem is that in some
instances the receiver may not have sufficient memory or other
storage to maintain such a large table of vertices. In some
implementations, there is an upper bound of 16 bits for
receiver-side vertex indices, maxing out the number of different
(or so the client-side device thinks) vertices at 216 (65,536).
[0401] To address this issue, in various different embodiments, in
addition to sending the mesh-vertices information to the rendering
device, PSS 202 also transmits one or more duplicate-vertex lists,
conveying in various different ways information that conveys
(though more tersely than this) messages such as "the nineteenth
vertex that I sent you is a duplicate of the fifth vertex that I
sent you, so you can ignore the nineteenth vertex." Thus, in at
least some embodiments, further aspects of mesh compression involve
informing the receiver-side device that certain vertices are really
duplicates or co-located in the shared geometry 1040 with
previously identified vertices.
[0402] In some embodiments, PSS 202 organizes one or more
duplicate-vertices-notification reports in the form of two-column
table, where each row contains the sequence number of two vertices
that have the same xyz coordinates in the shared geometry 1040. In
some embodiments, such reports are sent by PSS 202 during
intermediate time frames. And certainly other possible
implementations could be listed here as well.
[0403] 6. Transmission of Encoded Video Streams and Geometric-Data
Stream(s) to Rendering Device
[0404] At step 1610, PSS 202 transmits the at least M+1 separate
time-synchronized data streams to the HMD 112 for rendering of the
viewpoint-adaptive 3D persona 116 of the presenter 102. In this
particular example, PSS 202 transmits the encoded video streams
218L, 218C, and 218R, as well as the geometric-data stream 220LCR,
which, as described above, could be a single stream, could be three
separate streams 220L, 220C, and 220R, or perhaps some other
arrangement deemed suitable by those of skill in the art for
arranging the geometric information among one or more data streams
separate and apart from the streams conveying the color
information.
[0405] In various different embodiments, the color information
and/or the geometric information could be transmitted using the
Internet Protocol (IP) as the network-layer protocol and either the
Transport Control Protocol (TCP) or the User Datagram Protocol
(UDP) as the transport-layer protocol, among other options. As a
general matter, TCP/IP incurs more overhead than UDP/IP but
includes retransmission protocols to increase the likelihood of
delivery, while UDP/IP includes no such retransmission protocols
but incurs less overhead and therefore frees up more bandwidth.
Those of skill in the art are familiar with such tradeoffs. Other
protocols may be used as well, as deemed suitable by those of skill
in the art for a given implementation and/or in a given
context.
[0406] B. Example Receiver-Side Operation
[0407] FIG. 23 is a flowchart of a method 2300, in accordance with
at least one embodiment. By way of example, the method 2300 is
described below as being carried out by the HMD 112, though any
computing system or device, or combination of such systems and
devices, CCD or other device that is suitably equipped, programmed,
and configured could be used in various different implementations
to carry out the method 2300.
[0408] At step 2302, the HMD 112 receives time-synchronized video
frames of a subject (e.g., the presenter 102) that were captured by
video cameras (e.g., the camera assemblies 1024) at known locations
in a shared geometry such as the shared geometry 1040. In some
embodiments, the video frames arrive as raw video streams such as
the raw video streams 208. In other embodiments, the video frames
arrive at the HMD 112 as encoded video streams such as the encoded
video streams 218.
[0409] At step 2304, the HMD 112 obtains a time-synchronized 3D
mesh of the subject. In at least one embodiment, the HMD 112 may
carry out step 2304 of the method 2300 in any of the various ways
that are described above for PSS 202 carrying out step 1604 of the
method 1600. Thus, taken together, on a frame-by-frame basis, the
carrying out of steps 2302 and 2304 provides the HMD 112 with
full-color, full-resolution color images of the subject from, in
this example, three different vantage points in the shared geometry
(e.g., the vantage point 1080 of the camera assembly 1024L, the
vantage point 1082 of the camera assembly 1024C, and the vantage
point 1084 of the camera assembly 1024R).
[0410] At step 2306, HMD 112 identifies a user-selected viewpoint
for the shared geometry 1040. In various different embodiments, HMD
112 may carry out step 2306 on the basis of one or more factors
such as eye gaze, head tilt, head rotation, and/or any other
factors that are known in the art for determining a user-selected
viewpoint for a VR or AR experience.
[0411] At step 2308, HMD 112 calculates time-synchronized
visible-vertices lists, again on a
per-shared-frame-rate-time-period basis, from the vantage point of
at least each of the camera assemblies that is necessary to render
the 3D persona 116 based on the user-selected viewpoint that is
identified in step 2306. For the most part, HMD 112 may carry out
step 2308 of the method 2300 in any of the various ways that are
described above for PSS 202 carrying out step 1606 of the method
1600.
[0412] An exception to this in certain embodiments is that, while
PSS 202, in carrying out step 1606, calculates a visible-vertices
list from the perspective of each and every camera assembly 1024
(because PSS 202 does not know what viewpoint a user may select for
a given frame, and may in any event be streaming the data to
multiple viewers that are nearly certain to select at least
slightly different viewpoints in many frames), HMD 112, in some
embodiments of carrying out step 2308, only computes
visible-vertices lists from the vantage points of those camera
assemblies 1024 that will be needed to render the 3D persona from
the perspective of the user-selected viewpoint that is identified
in step 2306. In many cases, only two such visible-vertices lists
are needed.
[0413] At step 2310, HMD 112 projects the vertices from each
visible-vertices list that it calculated in step 2308 on to video
pixels (color-data pixels from RGB video cameras 1102 of camera
assemblies 1024) from the respective vantage points of the camera
assemblies 1024 that are associated with the visible-vertices lists
calculated in step 2308. Thus, using the type of geometry and
mathematics that are displayed in, and described in connection
with, FIGS. 17-19, the HMD determines, for each vertex in each
visible-vertices list, which color pixel in the corresponding RGB
video frame projects to that vertex in the shared geometry
1040.
[0414] At step 2312, the HMD 112 renders the viewpoint-adaptive 3D
presenter persona 116 of the subject (e.g., of the presenter 102)
using the geometric information from the visible-vertices lists
that the HMD 112 calculated in step 2308 and the color-pixel
information identified for such vertices in step 2310. HMD 112 may,
as is known in the art, carry out some geometric interpolation
between and among the vertices that are identified as visible in
step 2308.
[0415] If the HMD 112 is rendering the 3D persona in a given frame
based on two camera-assembly perspectives, the HMD 112 may first
render the submesh associated with the visible-vertices list of the
first of those two camera-assembly perspectives and then overlay a
rendering of the submesh associated with the visible-vertices list
of the second of those two camera-assembly perspectives. Serial
render-and-overlay sequence could be used for any number of
submeshes representing respective parts of the subject.
[0416] In some embodiments, as each successive submesh is overlaid
on the one or more that had been rendered already, the HMD 112
specifies the weighting percentages to give the new submesh as
compared with what has already been rendered. Thus, to get a 1/3
weighting result for each of three color values for a given vertex,
the HMD 112 may specify to use 100% weighting for the color
information from the first viewpoint for that vertex when rendering
the first submesh, then to go 50% percent weighting for color
information for that vertex from each of the second submesh and the
existing rendering, and then finally go to 67% weighting for color
information from the existing rendering for that vertex and 33%
weighting for color information from the third submesh for that
vertex. And certainly many other examples could be listed as
well.
[0417] In cases where the HMD 112 determines that a given vertex is
visible from two different perspectives, the HMD 112 may carry out
a process that is known in the art as texture blending, projective
texture blending, and the like. In accordance with that process,
the HMD 112 may render that vertex in a color that is a weighted
blend of the respective different color pixels that the HMD 112
projected on to that same 3D location in the shared geometry 1040
from however many camera-assembly perspectives are being blended in
the case of that given vertex. An example of texture-blending is
described in, e.g., U.S. Pat. No. 7,142,209, issued Nov. 28, 2006
to Uyttendaele et al. and is entitled "Real-Time Rendering System
and Process for Interactive Viewpoint Video that was Generated
Using Overlapping Images of a Scene Captured from Viewpoints
Forming a Grid," and which is hereby incorporated herein by
reference in its entirety.
[0418] FIG. 24 is a view of an example viewer-side arrangement 2400
that includes three example submesh virtual-projection viewpoints
2404L, 2404C, and 2404R that correspond with the three camera
assemblies 1024L, 1024C, and 1024R, in accordance with at least one
embodiment. Virtual-projection viewpoints 2404 are not physical
devices on the receiver side, but rather are placed in FIG. 24 to
correspond to the three respective viewpoints from which the
subject was captured on the sender side. For actual rendering
devices, in some embodiments, as is known in the art, the HMD 112
includes two rendering systems, one for each eye of the human user,
in which each such rendering system renders an eye-specific image
that is then stereoscopically combined naturally by the brain of
the user.
[0419] In FIG. 24, it can be seen that the rendered display is
represented by a simple icon 2402 that is not meant to convey any
particular detail, but rather to serve as a representation of the
common focal points of the virtual-projection viewpoints 2404L,
2404C, and 2404R. For the reader's convenience, example rays 2406L,
2406C, and 2406R are shown as respectively emanating from the
virtual-projection viewpoints 2404L, 2404C, and 2404R. Consistent
with the top-down view of the camera assemblies 1024 in FIG. 10C,
the rays 2406L and 2406C form a 45.degree. angle 2408, and the rays
2406C and 2406R form another 45.degree. angle 2410, therefore
combining into a 90.degree. angle.
[0420] FIG. 25-29 show respective views of five different example
user-selected viewpoints, and the resulting weighting percentages
that in at least one embodiment are given the various
virtual-projection viewpoints 2404L, 2404C, and 2404R in the
various different example scenarios. FIGS. 25-27 show three
different user-selected viewpoints in which one of the three
percentages is 100% and the other two are 0%. Thus, a given vertex
is visible from multiple ones of the virtual-projection viewpoints
2404L, 2404C, and 2404R. Only the color information associated with
one of those three virtual-projection viewpoints is used. Rather,
this division of percentages in those three figures is meant to
indicate that, from those three particular user-selected
viewpoints, there are no vertices that are visible from more than
one of the virtual-projection viewpoints.
[0421] FIG. 25 shows a view 2500 in which a perfectly centered
user-selected viewpoint 2502 (looking perfectly along the ray
2406C) results in a 100% usage of the color information for the
center virtual-projection viewpoint 2404C, 0% usage of the color
information for the left-side virtual projection viewpoint 2404L
and 0% usage of the color information for the right-side
virtual-projection viewpoint 2404R.
[0422] FIG. 26 shows a view 2600 in which a rightmost user-selected
viewpoint 2602 (looking along the ray 2406R) results in a 100%
usage of the color information for the right-side
virtual-projection viewpoint 2404R, 0% usage of the color
information for the center virtual projection viewpoint 2404C, and
0% usage of the color information for the left-side
virtual-projection viewpoint 2404L.
[0423] FIG. 27 shows a view 2700 in which a leftmost user-selected
viewpoint 2702 (looking perfectly along the ray 2406L) results in a
100% usage of the color information for the left-side
virtual-projection viewpoint 2404L, 0% usage of the color
information for the center virtual projection viewpoint 2404C, and
0% usage of the color information for the right-side
virtual-projection viewpoint 2404R.
[0424] FIG. 28 shows a view 2800 in which a user-selected viewpoint
2802 (looking along a ray 2804) is an intermediate viewpoint
between the center viewpoint 2502 of FIG. 25 and the leftmost
viewpoint 2702 of FIG. 27. In the example, this results in a
27.degree. angle 2806 between the ray 2406L and the ray 2804 and
also results in an 18.degree. angle 2808 between the ray 2804 and
the ray 2406C. As one might expect from a user-selected viewpoint
that is angularly closer to center, the resulting percentage
weighting (60%) given to pixel colors from the center virtual
projection viewpoint 2404C is greater than the percentage weighting
(40%) given in this example to color-pixel information from the
left-side virtual-projection viewpoint 2404L. In this example, the
center-viewpoint color information weight was derived by the
fraction 27.degree./45.degree., while the left-side viewpoint
color-information weight was derived by the complementary fraction
18.degree./45.degree.. Other approaches could be used.
[0425] FIG. 29 shows a view 2900 in which a user-selected viewpoint
2902 (looking along a ray 2904) is an intermediate viewpoint
between the center viewpoint 2502 of FIG. 25 and the rightmost
viewpoint 2602 of FIG. 26. In the example, this results in a
36.degree. angle 2906 between the ray 2406C and the ray 2904 and
also results in an 9.degree. angle 2908 between the ray 2904 and
the ray 2406R. From a user-selected viewpoint that is angularly
closer to the rightmost viewpoint than it is to the center
viewpoint, the resulting percentage weighting (80%) given to pixel
colors from the right-side virtual projection viewpoint 2404R is
greater than the percentage weighting (20%) given in this example
to color-pixel information from the center virtual-projection
viewpoint 2404C. In this example, the center-viewpoint color
information weight was derived by the fraction
9.degree./45.degree., while the right-side viewpoint
color-information weight was derived by the complementary fraction
36.degree./45.degree.. Certainly other approaches could be
used.
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